Unnamed: 0
int64 | category
string | githuburl
string | customtopics
string | customabout
string | customarxiv
string | custompypi
string | featured
float64 | links
string | description
string | _repopath
string | _reponame
string | _stars
int64 | _forks
int64 | _watches
int64 | _language
string | _homepage
string | _github_description
string | _organization
string | _updated_at
string | _created_at
string | _age_weeks
int64 | _stars_per_week
float64 | _avatar_url
string | _description
string | _github_topics
string | _topics
string | _last_commit_date
string | sim
string | _pop_contributor_count
int64 | _pop_contributor_orgs_len
float64 | _pop_contributor_orgs_error
float64 | _pop_commit_frequency
float64 | _pop_updated_issues_count
int64 | _pop_closed_issues_count
int64 | _pop_created_since_days
int64 | _pop_updated_since_days
int64 | _pop_recent_releases_count
int64 | _pop_recent_releases_estimated_tags
int64 | _pop_recent_releases_adjusted_count
int64 | _pop_issue_count
float64 | _pop_comment_count
float64 | _pop_comment_count_lookback_days
float64 | _pop_comment_frequency
float64 | _pop_score
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
450 | pandas | https://github.com/unionai-oss/pandera | [] | null | [] | [] | 1 | null | null | unionai-oss/pandera | pandera | 2,779 | 238 | 20 | Python | https://www.union.ai/pandera | A light-weight, flexible, and expressive statistical data testing library | unionai-oss | 2024-01-12 | 2018-11-01 | 273 | 10.152923 | https://avatars.githubusercontent.com/u/94206482?v=4 | A light-weight, flexible, and expressive statistical data testing library | ['assertions', 'data-assertions', 'data-check', 'data-cleaning', 'data-processing', 'data-validation', 'data-verification', 'dataframe-schema', 'dataframes', 'hypothesis-testing', 'pandas', 'pandas-dataframe', 'pandas-validation', 'pandas-validator', 'schema', 'testing', 'testing-tools', 'validation'] | ['assertions', 'data-assertions', 'data-check', 'data-cleaning', 'data-processing', 'data-validation', 'data-verification', 'dataframe-schema', 'dataframes', 'hypothesis-testing', 'pandas', 'pandas-dataframe', 'pandas-validation', 'pandas-validator', 'schema', 'testing', 'testing-tools', 'validation'] | 2023-12-15 | [('pandas-dev/pandas', 0.6850487589836121, 'pandas', 1), ('pyeve/cerberus', 0.6107540726661682, 'data', 1), ('ydataai/ydata-profiling', 0.5934801697731018, 'pandas', 2), ('hypothesisworks/hypothesis', 0.5685426592826843, 'testing', 1), ('python-odin/odin', 0.5636017322540283, 'util', 1), ('polyaxon/datatile', 0.55992591381073, 'pandas', 2), ('hi-primus/optimus', 0.557157576084137, 'ml-ops', 1), ('krzjoa/awesome-python-data-science', 0.5424999594688416, 'study', 0), ('tensorflow/data-validation', 0.5394289493560791, 'ml-ops', 0), ('dagworks-inc/hamilton', 0.5392426252365112, 'ml-ops', 1), ('datafold/data-diff', 0.5363107919692993, 'data', 0), ('great-expectations/great_expectations', 0.5351150035858154, 'ml-ops', 0), ('ydataai/ydata-quality', 0.5350058078765869, 'data', 1), ('plotly/dash', 0.5268102288246155, 'viz', 0), ('ibis-project/ibis', 0.525162398815155, 'data', 1), ('dylanhogg/awesome-python', 0.5203728079795837, 'study', 1), ('mementum/bta-lib', 0.5138059258460999, 'finance', 0), ('rasbt/mlxtend', 0.5126389265060425, 'ml', 0), ('man-group/dtale', 0.5104021430015564, 'viz', 1), ('lk-geimfari/mimesis', 0.509531557559967, 'data', 3), ('wolever/parameterized', 0.5074113607406616, 'testing', 0), ('tobymao/sqlglot', 0.5052539706230164, 'data', 0), ('saulpw/visidata', 0.5012566447257996, 'term', 1)] | 111 | 4 | null | 2.27 | 111 | 47 | 63 | 1 | 23 | 16 | 23 | 111 | 196 | 90 | 1.8 | 51 |
1,310 | study | https://github.com/promptslab/awesome-prompt-engineering | ['awesome'] | null | [] | [] | null | null | null | promptslab/awesome-prompt-engineering | Awesome-Prompt-Engineering | 2,728 | 231 | 49 | Python | https://discord.gg/m88xfYMbK6 | This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | promptslab | 2024-01-14 | 2023-02-09 | 50 | 53.791549 | https://avatars.githubusercontent.com/u/120981762?v=4 | This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | ['chatgpt', 'chatgpt-api', 'deep-learning', 'few-shot-learning', 'gpt', 'gpt-3', 'machine-learning', 'openai', 'prompt', 'prompt-based-learning', 'prompt-engineering', 'prompt-generator', 'prompt-learning', 'prompt-toolkit', 'prompt-tuning', 'promptengineering', 'text-to-image', 'text-to-speech', 'text-to-video'] | ['awesome', 'chatgpt', 'chatgpt-api', 'deep-learning', 'few-shot-learning', 'gpt', 'gpt-3', 'machine-learning', 'openai', 'prompt', 'prompt-based-learning', 'prompt-engineering', 'prompt-generator', 'prompt-learning', 'prompt-toolkit', 'prompt-tuning', 'promptengineering', 'text-to-image', 'text-to-speech', 'text-to-video'] | 2024-01-04 | [('promptslab/promptify', 0.7121634483337402, 'nlp', 8), ('microsoft/generative-ai-for-beginners', 0.6337803602218628, 'study', 4), ('bigscience-workshop/promptsource', 0.5903843641281128, 'nlp', 1), ('hegelai/prompttools', 0.5741844773292542, 'llm', 3), ('saharmor/dalle-playground', 0.5731377005577087, 'diffusion', 3), ('thudm/p-tuning-v2', 0.5705534815788269, 'nlp', 1), ('microsoft/promptbase', 0.56931471824646, 'llm', 1), ('karpathy/mingpt', 0.5419448018074036, 'llm', 0), ('microsoft/lmops', 0.538474440574646, 'llm', 2), ('keirp/automatic_prompt_engineer', 0.5347884297370911, 'llm', 1), ('agenta-ai/agenta', 0.53215092420578, 'llm', 2), ('ofa-sys/ofa', 0.5315465927124023, 'llm', 2), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5258796811103821, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.5253517031669617, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5253517031669617, 'llm', 0), ('nielsrogge/transformers-tutorials', 0.5090713500976562, 'study', 0), ('kyegomez/tree-of-thoughts', 0.5084075927734375, 'llm', 6), ('openai/image-gpt', 0.5061761140823364, 'llm', 0), ('openlmlab/moss', 0.5036895871162415, 'llm', 2), ('open-mmlab/mmediting', 0.5029655694961548, 'ml', 1), ('thilinarajapakse/simpletransformers', 0.5007800459861755, 'nlp', 0), ('ist-daslab/gptq', 0.5007389187812805, 'llm', 0)] | 13 | 8 | null | 2.08 | 3 | 3 | 11 | 0 | 0 | 0 | 0 | 3 | 0 | 90 | 0 | 51 |
1,254 | ml | https://github.com/huggingface/autotrain-advanced | [] | AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models | [] | [] | null | null | null | huggingface/autotrain-advanced | autotrain-advanced | 2,629 | 309 | 66 | Python | https://huggingface.co/autotrain | 🤗 AutoTrain Advanced | huggingface | 2024-01-13 | 2020-12-15 | 163 | 16.128834 | https://avatars.githubusercontent.com/u/25720743?v=4 | 🤗 AutoTrain Advanced | ['autotrain', 'deep-learning', 'huggingface', 'machine-learning', 'natural-language-processing', 'natural-language-understanding'] | ['autotrain', 'deep-learning', 'huggingface', 'machine-learning', 'natural-language-processing', 'natural-language-understanding'] | 2023-12-28 | [('awslabs/autogluon', 0.6629055142402649, 'ml', 3), ('winedarksea/autots', 0.6144011616706848, 'time-series', 2), ('keras-team/autokeras', 0.613167405128479, 'ml-dl', 2), ('nccr-itmo/fedot', 0.5916618704795837, 'ml-ops', 1), ('mosaicml/composer', 0.5858451724052429, 'ml-dl', 2), ('microsoft/nni', 0.5753588080406189, 'ml', 2), ('automl/auto-sklearn', 0.5570406317710876, 'ml', 0), ('huggingface/transformers', 0.5453631281852722, 'nlp', 3), ('alpa-projects/alpa', 0.5402303338050842, 'ml-dl', 2), ('huggingface/datasets', 0.5383889079093933, 'nlp', 3), ('microsoft/flaml', 0.5361581444740295, 'ml', 3), ('explosion/thinc', 0.5315551161766052, 'ml-dl', 3), ('xplainable/xplainable', 0.5284545421600342, 'ml-interpretability', 1), ('makcedward/nlpaug', 0.5261876583099365, 'nlp', 2), ('jindongwang/transferlearning', 0.5214440822601318, 'ml', 2), ('huggingface/huggingface_hub', 0.5198477506637573, 'ml', 3), ('deepfakes/faceswap', 0.5193936228752136, 'ml-dl', 2), ('thilinarajapakse/simpletransformers', 0.517383873462677, 'nlp', 0), ('alibaba/easynlp', 0.5123465657234192, 'nlp', 2), ('extreme-bert/extreme-bert', 0.5114012360572815, 'llm', 3), ('luodian/otter', 0.5102346539497375, 'llm', 2), ('docarray/docarray', 0.5089057087898254, 'data', 2), ('microsoft/unilm', 0.5079038143157959, 'nlp', 0), ('nvidia/deeplearningexamples', 0.5064716339111328, 'ml-dl', 1), ('google/trax', 0.5045042037963867, 'ml-dl', 2), ('torantulino/auto-gpt', 0.5032193660736084, 'llm', 0), ('microsoft/semi-supervised-learning', 0.5021505355834961, 'ml', 3), ('salesforce/blip', 0.5011047124862671, 'diffusion', 0), ('open-mmlab/mmediting', 0.5010733008384705, 'ml', 1), ('interpretml/interpret', 0.500921368598938, 'ml-interpretability', 1), ('christoschristofidis/awesome-deep-learning', 0.5007786750793457, 'study', 2), ('amanchadha/coursera-deep-learning-specialization', 0.500561535358429, 'study', 1)] | 19 | 4 | null | 7.81 | 255 | 236 | 37 | 1 | 0 | 0 | 0 | 255 | 618 | 90 | 2.4 | 51 |
1,553 | util | https://github.com/beeware/briefcase | ['package', 'python-to-exe', 'bundle'] | null | [] | [] | null | null | null | beeware/briefcase | briefcase | 2,206 | 310 | 45 | Python | https://briefcase.readthedocs.io/ | Tools to support converting a Python project into a standalone native application. | beeware | 2024-01-12 | 2015-07-28 | 444 | 4.968468 | https://avatars.githubusercontent.com/u/19795701?v=4 | Tools to support converting a Python project into a standalone native application. | [] | ['bundle', 'package', 'python-to-exe'] | 2024-01-14 | [('pyinstaller/pyinstaller', 0.6962027549743652, 'util', 3), ('ofek/pyapp', 0.6602802276611328, 'util', 1), ('indygreg/pyoxidizer', 0.6296486258506775, 'util', 0), ('pypa/hatch', 0.6087289452552795, 'util', 0), ('beeware/toga', 0.5662544965744019, 'gui', 0), ('pyodide/micropip', 0.561262845993042, 'util', 0), ('pypa/pipenv', 0.546190619468689, 'util', 0), ('pypy/pypy', 0.5429807901382446, 'util', 0), ('pypa/pipx', 0.5395711064338684, 'util', 0), ('mitsuhiko/rye', 0.5340247750282288, 'util', 0), ('willmcgugan/textual', 0.5323150753974915, 'term', 0), ('python-poetry/poetry', 0.5169381499290466, 'util', 0), ('connorferster/handcalcs', 0.5107748508453369, 'jupyter', 0), ('dosisod/refurb', 0.5095663666725159, 'util', 0), ('hoffstadt/dearpygui', 0.5077422261238098, 'gui', 0), ('r0x0r/pywebview', 0.5074366927146912, 'gui', 0), ('linkedin/shiv', 0.5067877173423767, 'util', 0), ('kubeflow/fairing', 0.5065484046936035, 'ml-ops', 0), ('samuelcolvin/python-devtools', 0.5053153038024902, 'debug', 0), ('pypa/flit', 0.5033259987831116, 'util', 0)] | 135 | 7 | null | 18.13 | 113 | 90 | 103 | 0 | 5 | 7 | 5 | 113 | 198 | 90 | 1.8 | 51 |
1,160 | jupyter | https://github.com/mito-ds/monorepo | [] | null | [] | [] | null | null | null | mito-ds/monorepo | mito | 2,096 | 143 | 22 | Python | https://trymito.io | The mitosheet package, trymito.io, and other public Mito code. | mito-ds | 2024-01-12 | 2022-01-20 | 105 | 19.827027 | https://avatars.githubusercontent.com/u/55325926?v=4 | The mitosheet package, trymito.io, and other public Mito code. | ['data', 'data-analysis', 'data-science', 'data-visualization', 'jupyter', 'pandas', 'streamlit-component'] | ['data', 'data-analysis', 'data-science', 'data-visualization', 'jupyter', 'pandas', 'streamlit-component'] | 2024-01-09 | [('holoviz/panel', 0.5779578685760498, 'viz', 1), ('saulpw/visidata', 0.5757395625114441, 'term', 1), ('polyaxon/datatile', 0.5668829083442688, 'pandas', 3), ('ta-lib/ta-lib-python', 0.5616872906684875, 'finance', 0), ('vaexio/vaex', 0.5610413551330566, 'perf', 1), ('rapidsai/cudf', 0.5557620525360107, 'pandas', 3), ('wesm/pydata-book', 0.5535025000572205, 'study', 0), ('man-group/dtale', 0.5518519282341003, 'viz', 4), ('kubeflow-kale/kale', 0.5466421842575073, 'ml-ops', 0), ('plotly/dash', 0.5446365475654602, 'viz', 3), ('jakevdp/pythondatasciencehandbook', 0.5431113243103027, 'study', 1), ('twopirllc/pandas-ta', 0.5410447716712952, 'finance', 1), ('pyqtgraph/pyqtgraph', 0.5288783311843872, 'viz', 0), ('fchollet/deep-learning-with-python-notebooks', 0.525928795337677, 'study', 0), ('gradio-app/gradio', 0.5249914526939392, 'viz', 3), ('geopandas/geopandas', 0.5150602459907532, 'gis', 1), ('krzjoa/awesome-python-data-science', 0.5128975510597229, 'study', 3), ('mwaskom/seaborn', 0.5123574733734131, 'viz', 3), ('alphasecio/langchain-examples', 0.5109399557113647, 'llm', 0), ('astral-sh/ruff', 0.5106766223907471, 'util', 0), ('lux-org/lux', 0.509769856929779, 'viz', 3), ('cohere-ai/notebooks', 0.5074410438537598, 'llm', 0), ('imageio/imageio', 0.5070230960845947, 'util', 0), ('scitools/iris', 0.5061976909637451, 'gis', 1), ('simonw/datasette', 0.5057903528213501, 'data', 0), ('pandas-dev/pandas', 0.5044918656349182, 'pandas', 3), ('delta-io/delta-rs', 0.503544807434082, 'pandas', 1), ('jovianml/opendatasets', 0.5031294822692871, 'data', 1), ('mementum/bta-lib', 0.5002267360687256, 'finance', 0)] | 7 | 2 | null | 42.33 | 272 | 214 | 24 | 0 | 0 | 0 | 0 | 272 | 366 | 90 | 1.3 | 51 |
316 | gamedev | https://github.com/pyglet/pyglet | [] | null | [] | [] | null | null | null | pyglet/pyglet | pyglet | 1,675 | 282 | 31 | Python | http://pyglet.org | pyglet is a cross-platform windowing and multimedia library for Python, for developing games and other visually rich applications. | pyglet | 2024-01-13 | 2019-06-09 | 242 | 6.913325 | https://avatars.githubusercontent.com/u/51539834?v=4 | pyglet is a cross-platform windowing and multimedia library for Python, for developing games and other visually rich applications. | ['gamedev', 'opengl', 'pyglet', 'scientific-visualization'] | ['gamedev', 'opengl', 'pyglet', 'scientific-visualization'] | 2024-01-13 | [('pygame/pygame', 0.717469334602356, 'gamedev', 1), ('hoffstadt/dearpygui', 0.6631710529327393, 'gui', 0), ('pypy/pypy', 0.6459312438964844, 'util', 0), ('pysimplegui/pysimplegui', 0.624721884727478, 'gui', 0), ('python/cpython', 0.5984718799591064, 'util', 0), ('pygamelib/pygamelib', 0.5976749062538147, 'gamedev', 1), ('r0x0r/pywebview', 0.593908965587616, 'gui', 0), ('viblo/pymunk', 0.5925350189208984, 'sim', 1), ('wxwidgets/phoenix', 0.5862021446228027, 'gui', 0), ('urwid/urwid', 0.5836073160171509, 'term', 0), ('webpy/webpy', 0.5836023688316345, 'web', 0), ('maartenbreddels/ipyvolume', 0.580999493598938, 'jupyter', 1), ('holoviz/holoviz', 0.5789063572883606, 'viz', 0), ('beeware/toga', 0.572860598564148, 'gui', 0), ('altair-viz/altair', 0.5653769969940186, 'viz', 0), ('imageio/imageio', 0.5611344575881958, 'util', 0), ('pyston/pyston', 0.5540895462036133, 'util', 0), ('pygments/pygments', 0.5452966094017029, 'util', 0), ('jquast/blessed', 0.5451313853263855, 'term', 0), ('lordmauve/pgzero', 0.544232964515686, 'gamedev', 0), ('graphistry/pygraphistry', 0.5440356135368347, 'data', 0), ('eleutherai/pyfra', 0.5430480241775513, 'ml', 0), ('pympler/pympler', 0.5425298810005188, 'perf', 0), ('pytoolz/toolz', 0.5421815514564514, 'util', 0), ('pypa/virtualenv', 0.541969895362854, 'util', 0), ('inducer/pudb', 0.5417070984840393, 'debug', 0), ('kivy/kivy', 0.5390869975090027, 'util', 0), ('matplotlib/matplotlib', 0.5357295870780945, 'viz', 0), ('pylons/pyramid', 0.5355060696601868, 'web', 0), ('bokeh/bokeh', 0.5342501401901245, 'viz', 0), ('alexmojaki/snoop', 0.5321446061134338, 'debug', 0), ('parthjadhav/tkinter-designer', 0.5310909748077393, 'gui', 0), ('pythonarcade/arcade', 0.5264350175857544, 'gamedev', 1), ('willmcgugan/textual', 0.5208635330200195, 'term', 0), ('connorferster/handcalcs', 0.5195719003677368, 'jupyter', 0), ('gboeing/pynamical', 0.5189253091812134, 'sim', 0), ('alexmojaki/heartrate', 0.5163092613220215, 'debug', 0), ('opengeos/leafmap', 0.5158253312110901, 'gis', 0), ('bottlepy/bottle', 0.5153478384017944, 'web', 0), ('zulko/moviepy', 0.5143889784812927, 'util', 0), ('panda3d/panda3d', 0.5137691497802734, 'gamedev', 2), ('vispy/vispy', 0.5124850273132324, 'viz', 1), ('wesm/pydata-book', 0.5112947225570679, 'study', 0), ('pyodide/pyodide', 0.5096766948699951, 'util', 0), ('scitools/cartopy', 0.5082891583442688, 'gis', 0), ('holoviz/geoviews', 0.5069261789321899, 'gis', 0), ('py4j/py4j', 0.5067098140716553, 'util', 0), ('holoviz/panel', 0.5058193206787109, 'viz', 0), ('python-pillow/pillow', 0.505748450756073, 'util', 0), ('erotemic/ubelt', 0.5050175786018372, 'util', 0), ('jiffyclub/snakeviz', 0.5042890310287476, 'profiling', 0), ('plotly/plotly.py', 0.5040709972381592, 'viz', 0), ('kanaries/pygwalker', 0.5040571689605713, 'pandas', 0), ('landscapeio/prospector', 0.5031312108039856, 'util', 0)] | 156 | 4 | null | 5 | 92 | 69 | 56 | 0 | 8 | 13 | 8 | 92 | 193 | 90 | 2.1 | 51 |
1,563 | llm | https://github.com/ray-project/llm-applications | ['rag'] | null | [] | [] | null | null | null | ray-project/llm-applications | llm-applications | 1,207 | 135 | 14 | Jupyter Notebook | null | A comprehensive guide to building RAG-based LLM applications for production. | ray-project | 2024-01-13 | 2023-08-16 | 23 | 50.592814 | https://avatars.githubusercontent.com/u/22125274?v=4 | A comprehensive guide to building RAG-based LLM applications for production. | ['anyscale', 'fine-tuning', 'llama2', 'llms', 'machine-learning', 'openai', 'ray', 'serving'] | ['anyscale', 'fine-tuning', 'llama2', 'llms', 'machine-learning', 'openai', 'rag', 'ray', 'serving'] | 2024-01-08 | [('alpha-vllm/llama2-accessory', 0.6254207491874695, 'llm', 1), ('bentoml/openllm', 0.6087220311164856, 'ml-ops', 2), ('tigerlab-ai/tiger', 0.6013271808624268, 'llm', 2), ('h2oai/h2o-llmstudio', 0.5635949373245239, 'llm', 2), ('run-llama/llama-hub', 0.5521401166915894, 'data', 0), ('eugeneyan/open-llms', 0.5464956760406494, 'study', 1), ('hiyouga/llama-factory', 0.5320853590965271, 'llm', 2), ('hiyouga/llama-efficient-tuning', 0.5320852994918823, 'llm', 2), ('jerryjliu/llama_index', 0.5219264030456543, 'llm', 2), ('langchain-ai/langsmith-cookbook', 0.515418529510498, 'llm', 0), ('tloen/alpaca-lora', 0.5138564109802246, 'llm', 0), ('berriai/litellm', 0.5112382173538208, 'llm', 1), ('deepset-ai/haystack', 0.5103896260261536, 'llm', 1), ('vllm-project/vllm', 0.5022127032279968, 'llm', 0)] | 4 | 3 | null | 1.33 | 28 | 21 | 5 | 0 | 8 | 22 | 8 | 28 | 5 | 90 | 0.2 | 51 |
1,763 | data | https://github.com/dlt-hub/dlt | ['duckdb', 'data-engineering'] | null | [] | [] | null | null | null | dlt-hub/dlt | dlt | 1,028 | 71 | 14 | Python | https://dlthub.com/docs | data load tool (dlt) is an open source Python library that makes data loading easy 🛠️ | dlt-hub | 2024-01-13 | 2022-01-26 | 104 | 9.803815 | https://avatars.githubusercontent.com/u/89419010?v=4 | data load tool (dlt) is an open source Python library that makes data loading easy 🛠️ | ['data', 'data-engineering', 'data-lake', 'data-loading', 'data-warehouse', 'elt', 'extract', 'load', 'transform'] | ['data', 'data-engineering', 'data-lake', 'data-loading', 'data-warehouse', 'duckdb', 'elt', 'extract', 'load', 'transform'] | 2024-01-12 | [('pandas-dev/pandas', 0.554896891117096, 'pandas', 0), ('dbt-labs/dbt-core', 0.5388659834861755, 'ml-ops', 1), ('pytables/pytables', 0.5339342355728149, 'data', 0), ('airbytehq/airbyte', 0.5255663394927979, 'data', 3), ('erotemic/ubelt', 0.5195381045341492, 'util', 0), ('holoviz/panel', 0.5181348323822021, 'viz', 0), ('pytorch/data', 0.5167723298072815, 'data', 0), ('databricks/dbt-databricks', 0.5166671276092529, 'data', 0), ('jovianml/opendatasets', 0.5093324184417725, 'data', 0), ('wesm/pydata-book', 0.5088979005813599, 'study', 0), ('imageio/imageio', 0.5059615969657898, 'util', 0), ('dagworks-inc/hamilton', 0.5049058198928833, 'ml-ops', 1), ('intake/intake', 0.5047716498374939, 'data', 0), ('saulpw/visidata', 0.5002819299697876, 'term', 0)] | 39 | 3 | null | 28.83 | 245 | 189 | 24 | 0 | 46 | 34 | 46 | 243 | 289 | 90 | 1.2 | 51 |
864 | pandas | https://github.com/eventual-inc/daft | [] | null | [] | [] | null | null | null | eventual-inc/daft | Daft | 987 | 57 | 11 | Rust | https://getdaft.io | Distributed DataFrames for Python designed for the cloud, powered by Rust | eventual-inc | 2024-01-12 | 2022-04-25 | 92 | 10.711628 | https://avatars.githubusercontent.com/u/98941975?v=4 | Distributed DataFrames for Python designed for the cloud, powered by Rust | ['data-engineering', 'data-science', 'dataframe', 'deep-learning', 'distributed-computing', 'image-processing', 'machine-learning', 'rust'] | ['data-engineering', 'data-science', 'dataframe', 'deep-learning', 'distributed-computing', 'image-processing', 'machine-learning', 'rust'] | 2024-01-13 | [('backtick-se/cowait', 0.7438012361526489, 'util', 2), ('pola-rs/polars', 0.7005141377449036, 'pandas', 2), ('merantix-momentum/squirrel-core', 0.6807416081428528, 'ml', 3), ('fugue-project/fugue', 0.6778815388679504, 'pandas', 2), ('horovod/horovod', 0.6405372023582458, 'ml-ops', 2), ('delta-io/delta-rs', 0.6028104424476624, 'pandas', 1), ('fastai/fastcore', 0.572355329990387, 'util', 0), ('vaexio/vaex', 0.5715435743331909, 'perf', 3), ('dagworks-inc/hamilton', 0.5710514783859253, 'ml-ops', 4), ('dylanhogg/awesome-python', 0.5679042935371399, 'study', 3), ('sfu-db/connector-x', 0.5663425326347351, 'data', 2), ('gradio-app/gradio', 0.5626286864280701, 'viz', 3), ('lithops-cloud/lithops', 0.5597764849662781, 'ml-ops', 0), ('uber/petastorm', 0.557521641254425, 'data', 2), ('jmcarpenter2/swifter', 0.55370032787323, 'pandas', 0), ('polyaxon/datatile', 0.5458943843841553, 'pandas', 1), ('tensorflow/tensorflow', 0.5434378385543823, 'ml-dl', 2), ('krzjoa/awesome-python-data-science', 0.5433785915374756, 'study', 3), ('rapidsai/cudf', 0.5403209924697876, 'pandas', 2), ('uber/fiber', 0.5396055579185486, 'data', 2), ('netflix/metaflow', 0.5369682312011719, 'ml-ops', 2), ('hi-primus/optimus', 0.5366455316543579, 'ml-ops', 2), ('orchest/orchest', 0.5366385579109192, 'ml-ops', 2), ('pyo3/maturin', 0.5351252555847168, 'util', 1), ('aws/chalice', 0.5330685973167419, 'web', 0), ('apache/spark', 0.5322152972221375, 'data', 0), ('google/tf-quant-finance', 0.5302774906158447, 'finance', 0), ('pyinfra-dev/pyinfra', 0.5292609333992004, 'util', 0), ('eleutherai/pyfra', 0.5290564894676208, 'ml', 0), ('dask/dask', 0.5266561508178711, 'perf', 0), ('klen/muffin', 0.5256049036979675, 'web', 0), ('adap/flower', 0.5243489146232605, 'ml-ops', 2), ('aws/sagemaker-python-sdk', 0.5235341787338257, 'ml', 1), ('paddlepaddle/paddle', 0.5211067199707031, 'ml-dl', 2), ('pytables/pytables', 0.5193186402320862, 'data', 0), ('mlflow/mlflow', 0.5185021162033081, 'ml-ops', 1), ('nalepae/pandarallel', 0.5181572437286377, 'pandas', 0), ('masoniteframework/masonite', 0.5179644227027893, 'web', 0), ('darribas/gds_env', 0.5175384879112244, 'gis', 0), ('pandas-dev/pandas', 0.5169585347175598, 'pandas', 2), ('nevronai/metisfl', 0.515454113483429, 'ml', 2), ('kubeflow-kale/kale', 0.5150558352470398, 'ml-ops', 1), ('rustpython/rustpython', 0.5145300626754761, 'util', 1), ('determined-ai/determined', 0.5144329071044922, 'ml-ops', 3), ('cython/cython', 0.5138062238693237, 'util', 0), ('ydataai/ydata-profiling', 0.5120884776115417, 'pandas', 3), ('pallets/flask', 0.5096858143806458, 'web', 0), ('aws/aws-sdk-pandas', 0.5070556998252869, 'pandas', 2), ('polyaxon/polyaxon', 0.5069470405578613, 'ml-ops', 3), ('online-ml/river', 0.506720244884491, 'ml', 2), ('panda3d/panda3d', 0.5059236288070679, 'gamedev', 0), ('google/gin-config', 0.5050129890441895, 'util', 0), ('nficano/python-lambda', 0.5019513964653015, 'util', 0), ('jina-ai/jina', 0.5019222497940063, 'ml', 2), ('falconry/falcon', 0.5012557506561279, 'web', 0)] | 17 | 3 | null | 17.12 | 327 | 270 | 21 | 0 | 30 | 33 | 30 | 325 | 375 | 90 | 1.2 | 51 |
624 | gis | https://github.com/pytroll/satpy | [] | null | [] | [] | null | null | null | pytroll/satpy | satpy | 978 | 277 | 35 | Python | http://satpy.readthedocs.org/en/latest/ | Python package for earth-observing satellite data processing | pytroll | 2024-01-12 | 2016-02-09 | 416 | 2.350962 | https://avatars.githubusercontent.com/u/13004956?v=4 | Python package for earth-observing satellite data processing | ['dask', 'satellite', 'weather', 'xarray'] | ['dask', 'satellite', 'weather', 'xarray'] | 2024-01-10 | [('sentinel-hub/eo-learn', 0.6639524102210999, 'gis', 0), ('scitools/iris', 0.6135034561157227, 'gis', 0), ('sentinel-hub/sentinelhub-py', 0.6114635467529297, 'gis', 0), ('radiantearth/radiant-mlhub', 0.5896352529525757, 'gis', 0), ('cloudsen12/easystac', 0.5563086271286011, 'gis', 0), ('opengeos/earthformer', 0.5178881287574768, 'gis', 0), ('autoviml/auto_ts', 0.5144683718681335, 'time-series', 0), ('giswqs/geemap', 0.5017055869102478, 'gis', 0)] | 149 | 7 | null | 28.92 | 152 | 90 | 96 | 0 | 11 | 12 | 11 | 152 | 631 | 90 | 4.2 | 51 |
1,737 | llm | https://github.com/akariasai/self-rag | ['rag', 'retrieval'] | null | [] | [] | null | null | null | akariasai/self-rag | self-rag | 946 | 66 | 9 | Python | https://selfrag.github.io/ | This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. | akariasai | 2024-01-13 | 2023-10-10 | 16 | 59.125 | null | This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. | [] | ['rag', 'retrieval'] | 2023-12-22 | [] | 7 | 2 | null | 0.54 | 35 | 24 | 3 | 1 | 0 | 0 | 0 | 35 | 107 | 90 | 3.1 | 51 |
805 | nlp | https://github.com/gunthercox/chatterbot | [] | null | [] | [] | null | null | null | gunthercox/chatterbot | ChatterBot | 13,767 | 4,453 | 553 | Python | https://chatterbot.readthedocs.io | ChatterBot is a machine learning, conversational dialog engine for creating chat bots | gunthercox | 2024-01-13 | 2014-09-28 | 487 | 28.252419 | null | ChatterBot is a machine learning, conversational dialog engine for creating chat bots | ['bot', 'chatbot', 'chatterbot', 'conversation', 'language', 'machine-learning'] | ['bot', 'chatbot', 'chatterbot', 'conversation', 'language', 'machine-learning'] | 2021-06-01 | [('togethercomputer/openchatkit', 0.6964467167854309, 'nlp', 1), ('rasahq/rasa', 0.6935678720474243, 'llm', 3), ('deeppavlov/deeppavlov', 0.6284914016723633, 'nlp', 3), ('rcgai/simplyretrieve', 0.5998932123184204, 'llm', 1), ('gunthercox/chatterbot-corpus', 0.5911657214164734, 'nlp', 2), ('nomic-ai/gpt4all', 0.5897710919380188, 'llm', 1), ('nvidia/nemo', 0.5878262519836426, 'nlp', 0), ('embedchain/embedchain', 0.5760533809661865, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5590612888336182, 'llm', 1), ('lm-sys/fastchat', 0.5577590465545654, 'llm', 1), ('errbotio/errbot', 0.5522263646125793, 'nlp', 1), ('minimaxir/simpleaichat', 0.5406122207641602, 'llm', 0), ('databrickslabs/dolly', 0.5377427339553833, 'llm', 1), ('cheshire-cat-ai/core', 0.53544682264328, 'llm', 1), ('deepset-ai/haystack', 0.5347136855125427, 'llm', 1), ('pathwaycom/llm-app', 0.5325236916542053, 'llm', 2), ('openai/gpt-discord-bot', 0.5279523730278015, 'llm', 0), ('blinkdl/chatrwkv', 0.5218847393989563, 'llm', 1), ('kalliope-project/kalliope', 0.518312394618988, 'util', 1), ('krohling/bondai', 0.5118715763092041, 'llm', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5103239417076111, 'llm', 0), ('chatarena/chatarena', 0.5086801052093506, 'llm', 0)] | 103 | 5 | null | 0 | 24 | 1 | 113 | 32 | 0 | 9 | 9 | 24 | 23 | 90 | 1 | 50 |
676 | diffusion | https://github.com/compvis/latent-diffusion | ['diffusion', 'image-generation'] | null | [] | [] | null | null | null | compvis/latent-diffusion | latent-diffusion | 9,627 | 1,286 | 89 | Jupyter Notebook | null | High-Resolution Image Synthesis with Latent Diffusion Models | compvis | 2024-01-14 | 2021-12-20 | 110 | 87.404669 | https://avatars.githubusercontent.com/u/30233788?v=4 | High-Resolution Image Synthesis with Latent Diffusion Models | [] | ['diffusion', 'image-generation'] | 2022-07-26 | [('stability-ai/stablediffusion', 1.0000004768371582, 'diffusion', 2), ('albarji/mixture-of-diffusers', 0.6906744837760925, 'diffusion', 0), ('compvis/stable-diffusion', 0.655044674873352, 'diffusion', 2), ('openai/glide-text2im', 0.6436025500297546, 'diffusion', 0), ('huggingface/diffusers', 0.6299404501914978, 'diffusion', 2), ('sharonzhou/long_stable_diffusion', 0.5753589272499084, 'diffusion', 0), ('openai/point-e', 0.5376940369606018, 'util', 0), ('timothybrooks/instruct-pix2pix', 0.5207135081291199, 'diffusion', 0), ('kakaobrain/rq-vae-transformer', 0.5072487592697144, 'ml-dl', 0)] | 5 | 1 | null | 0 | 67 | 13 | 25 | 18 | 0 | 0 | 0 | 67 | 147 | 90 | 2.2 | 50 |
135 | ml-dl | https://github.com/keras-team/autokeras | [] | null | [] | [] | null | null | null | keras-team/autokeras | autokeras | 9,016 | 1,419 | 303 | Python | http://autokeras.com/ | AutoML library for deep learning | keras-team | 2024-01-13 | 2017-11-19 | 323 | 27.888643 | https://avatars.githubusercontent.com/u/34455048?v=4 | AutoML library for deep learning | ['autodl', 'automated-machine-learning', 'automl', 'deep-learning', 'keras', 'machine-learning', 'neural-architecture-search', 'tensorflow'] | ['autodl', 'automated-machine-learning', 'automl', 'deep-learning', 'keras', 'machine-learning', 'neural-architecture-search', 'tensorflow'] | 2023-10-02 | [('microsoft/nni', 0.8086925148963928, 'ml', 6), ('awslabs/autogluon', 0.7593497633934021, 'ml', 4), ('microsoft/flaml', 0.7519674897193909, 'ml', 4), ('rafiqhasan/auto-tensorflow', 0.6616485714912415, 'ml-dl', 3), ('mljar/mljar-supervised', 0.6605311036109924, 'ml', 3), ('winedarksea/autots', 0.6600058078765869, 'time-series', 3), ('alpa-projects/alpa', 0.6452708840370178, 'ml-dl', 2), ('nccr-itmo/fedot', 0.6287457346916199, 'ml-ops', 3), ('explosion/thinc', 0.6246259808540344, 'ml-dl', 3), ('tensorlayer/tensorlayer', 0.622309148311615, 'ml-rl', 2), ('automl/auto-sklearn', 0.616755485534668, 'ml', 2), ('huggingface/autotrain-advanced', 0.613167405128479, 'ml', 2), ('ggerganov/ggml', 0.6085128784179688, 'ml', 1), ('tensorflow/tensor2tensor', 0.5915209650993347, 'ml', 2), ('onnx/onnx', 0.5896108746528625, 'ml', 4), ('neuralmagic/sparseml', 0.5828292369842529, 'ml-dl', 3), ('karpathy/micrograd', 0.5800349712371826, 'study', 0), ('huggingface/transformers', 0.5780196189880371, 'nlp', 3), ('huggingface/datasets', 0.5751702785491943, 'nlp', 3), ('nvidia/deeplearningexamples', 0.5739009976387024, 'ml-dl', 2), ('activeloopai/deeplake', 0.5705196857452393, 'ml-ops', 3), ('tensorflow/tensorflow', 0.5696538090705872, 'ml-dl', 3), ('keras-team/keras', 0.5688300728797913, 'ml-dl', 3), ('featurelabs/featuretools', 0.5649420022964478, 'ml', 3), ('ray-project/ray', 0.5593129396438599, 'ml-ops', 4), ('rasbt/machine-learning-book', 0.5562219023704529, 'study', 2), ('ludwig-ai/ludwig', 0.5494021773338318, 'ml-ops', 2), ('apple/coremltools', 0.546789288520813, 'ml', 2), ('mosaicml/composer', 0.5466687083244324, 'ml-dl', 2), ('lutzroeder/netron', 0.5447315573692322, 'ml', 4), ('d2l-ai/d2l-en', 0.5389357805252075, 'study', 4), ('google/automl', 0.5386403799057007, 'ml', 1), ('keras-team/keras-nlp', 0.5352697372436523, 'nlp', 4), ('ashleve/lightning-hydra-template', 0.534925103187561, 'util', 1), ('mlflow/mlflow', 0.5325337052345276, 'ml-ops', 1), ('uber/petastorm', 0.5320855975151062, 'data', 3), ('google/trax', 0.5302404761314392, 'ml-dl', 2), ('shankarpandala/lazypredict', 0.5269849896430969, 'ml', 2), ('zenml-io/zenml', 0.5269325375556946, 'ml-ops', 4), ('huggingface/exporters', 0.5266240239143372, 'ml', 3), ('microsoft/deepspeed', 0.5261964201927185, 'ml-dl', 2), ('deepmind/dm-haiku', 0.5257297158241272, 'ml-dl', 2), ('aws/sagemaker-python-sdk', 0.5244501233100891, 'ml', 2), ('polyaxon/polyaxon', 0.5219746232032776, 'ml-ops', 4), ('keras-rl/keras-rl', 0.5215295553207397, 'ml-rl', 3), ('xplainable/xplainable', 0.5181999802589417, 'ml-interpretability', 1), ('lucidrains/toolformer-pytorch', 0.516575276851654, 'llm', 1), ('pytorch/pytorch', 0.5157300233840942, 'ml-dl', 2), ('merantix-momentum/squirrel-core', 0.5147408246994019, 'ml', 3), ('microsoft/semi-supervised-learning', 0.514286458492279, 'ml', 2), ('horovod/horovod', 0.5139292478561401, 'ml-ops', 4), ('epistasislab/tpot', 0.5138368010520935, 'ml', 3), ('ourownstory/neural_prophet', 0.5104817748069763, 'ml', 2), ('aistream-peelout/flow-forecast', 0.5103901624679565, 'time-series', 1), ('oml-team/open-metric-learning', 0.5088566541671753, 'ml', 1), ('salesforce/deeptime', 0.5080786347389221, 'time-series', 1), ('microsoft/onnxruntime', 0.507604718208313, 'ml', 3), ('google/tf-quant-finance', 0.5068373680114746, 'finance', 1), ('googlecloudplatform/vertex-ai-samples', 0.5066843032836914, 'ml', 0), ('patchy631/machine-learning', 0.5066499710083008, 'ml', 0), ('pytorch/ignite', 0.5053526759147644, 'ml-dl', 2), ('iperov/deepfacelab', 0.5044635534286499, 'ml-dl', 2), ('amanchadha/coursera-deep-learning-specialization', 0.5036728382110596, 'study', 1), ('albumentations-team/albumentations', 0.5026241540908813, 'ml-dl', 2), ('torantulino/auto-gpt', 0.5023127198219299, 'llm', 0), ('lightly-ai/lightly', 0.5014397501945496, 'ml', 2), ('intel/intel-extension-for-pytorch', 0.5002623200416565, 'perf', 2)] | 143 | 4 | null | 0.37 | 5 | 0 | 75 | 3 | 1 | 9 | 1 | 5 | 7 | 90 | 1.4 | 50 |
942 | web | https://github.com/bottlepy/bottle | [] | null | [] | [] | null | null | null | bottlepy/bottle | bottle | 8,217 | 1,471 | 312 | Python | http://bottlepy.org/ | bottle.py is a fast and simple micro-framework for python web-applications. | bottlepy | 2024-01-13 | 2009-06-30 | 761 | 10.797635 | https://avatars.githubusercontent.com/u/1019799?v=4 | bottle.py is a fast and simple micro-framework for python web-applications. | ['bottle', 'rest', 'web-framework', 'wsgi'] | ['bottle', 'rest', 'web-framework', 'wsgi'] | 2024-01-03 | [('pallets/flask', 0.8060166835784912, 'web', 2), ('webpy/webpy', 0.7816590070724487, 'web', 0), ('pylons/pyramid', 0.7196846008300781, 'web', 2), ('cherrypy/cherrypy', 0.6888998746871948, 'web', 0), ('pallets/werkzeug', 0.6876417994499207, 'web', 1), ('masoniteframework/masonite', 0.6854233741760254, 'web', 0), ('falconry/falcon', 0.677134096622467, 'web', 2), ('willmcgugan/textual', 0.661994218826294, 'term', 0), ('reflex-dev/reflex', 0.6463294625282288, 'web', 0), ('scrapy/scrapy', 0.6345263123512268, 'data', 0), ('python-restx/flask-restx', 0.628285825252533, 'web', 1), ('eleutherai/pyfra', 0.6280529499053955, 'ml', 0), ('pypy/pypy', 0.623259961605072, 'util', 0), ('klen/muffin', 0.6195411086082458, 'web', 0), ('benoitc/gunicorn', 0.6153336763381958, 'web', 1), ('pyodide/pyodide', 0.6117225289344788, 'util', 0), ('pallets/quart', 0.5997056365013123, 'web', 0), ('pyeve/eve', 0.594473659992218, 'web', 1), ('pyinfra-dev/pyinfra', 0.590758204460144, 'util', 0), ('pylons/waitress', 0.5867997407913208, 'web', 0), ('neoteroi/blacksheep', 0.5855295062065125, 'web', 0), ('pyodide/micropip', 0.5800544023513794, 'util', 0), ('simple-salesforce/simple-salesforce', 0.5755710601806641, 'data', 0), ('r0x0r/pywebview', 0.5649958252906799, 'gui', 0), ('ets-labs/python-dependency-injector', 0.5632432699203491, 'util', 0), ('ethereum/web3.py', 0.5624367594718933, 'crypto', 0), ('flet-dev/flet', 0.5521423816680908, 'web', 0), ('1200wd/bitcoinlib', 0.5447421669960022, 'crypto', 0), ('requests/toolbelt', 0.5425326228141785, 'util', 0), ('hoffstadt/dearpygui', 0.541211724281311, 'gui', 0), ('stephenmcd/mezzanine', 0.5408180952072144, 'web', 0), ('encode/uvicorn', 0.5337467193603516, 'web', 0), ('timofurrer/awesome-asyncio', 0.5336396098136902, 'study', 0), ('indico/indico', 0.5327122211456299, 'web', 0), ('clips/pattern', 0.5306470990180969, 'nlp', 0), ('encode/httpx', 0.5296392440795898, 'web', 0), ('man-c/pycoingecko', 0.5294601917266846, 'crypto', 0), ('django/django', 0.5293058156967163, 'web', 0), ('dddomodossola/remi', 0.5286058187484741, 'gui', 0), ('klen/py-frameworks-bench', 0.5283790826797485, 'perf', 0), ('pypa/hatch', 0.5236046314239502, 'util', 0), ('plotly/dash', 0.5221378803253174, 'viz', 0), ('vitalik/django-ninja', 0.5206802487373352, 'web', 0), ('backtick-se/cowait', 0.5192388892173767, 'util', 0), ('fastai/fastcore', 0.5190497040748596, 'util', 0), ('tiangolo/sqlmodel', 0.5186842679977417, 'data', 0), ('tornadoweb/tornado', 0.5174039006233215, 'web', 0), ('dylanhogg/awesome-python', 0.5162600874900818, 'study', 0), ('holoviz/panel', 0.5157780647277832, 'viz', 0), ('pyglet/pyglet', 0.5153478384017944, 'gamedev', 0), ('indygreg/pyoxidizer', 0.514263391494751, 'util', 0), ('goldmansachs/gs-quant', 0.5141693949699402, 'finance', 0), ('feincms/feincms', 0.5126698613166809, 'web', 0), ('sqlalchemy/mako', 0.5123538970947266, 'template', 0), ('alirn76/panther', 0.5107240676879883, 'web', 0), ('voila-dashboards/voila', 0.5106417536735535, 'jupyter', 0), ('urwid/urwid', 0.5075056552886963, 'term', 0), ('pycqa/pylint-django', 0.5043782591819763, 'util', 0), ('rawheel/fastapi-boilerplate', 0.5016167759895325, 'web', 0)] | 226 | 8 | null | 0.02 | 12 | 6 | 177 | 5 | 0 | 6 | 6 | 12 | 27 | 90 | 2.2 | 50 |
96 | perf | https://github.com/vaexio/vaex | [] | null | [] | [] | null | null | null | vaexio/vaex | vaex | 8,106 | 590 | 145 | Python | https://vaex.io | Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀 | vaexio | 2024-01-14 | 2014-09-27 | 487 | 16.630129 | https://avatars.githubusercontent.com/u/45720408?v=4 | Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀 | ['bigdata', 'data-science', 'dataframe', 'hdf5', 'machine-learning', 'machinelearning', 'memory-mapped-file', 'pyarrow', 'tabular-data', 'visualization'] | ['bigdata', 'data-science', 'dataframe', 'hdf5', 'machine-learning', 'machinelearning', 'memory-mapped-file', 'pyarrow', 'tabular-data', 'visualization'] | 2023-07-21 | [('apache/arrow', 0.6739583611488342, 'data', 0), ('blaze/blaze', 0.6084710359573364, 'pandas', 0), ('ibis-project/ibis', 0.6082955598831177, 'data', 1), ('man-group/dtale', 0.6020064353942871, 'viz', 2), ('pytables/pytables', 0.6015515923500061, 'data', 0), ('holoviz/panel', 0.5968863368034363, 'viz', 0), ('pyqtgraph/pyqtgraph', 0.592536449432373, 'viz', 1), ('fastai/fastcore', 0.5877453088760376, 'util', 0), ('pola-rs/polars', 0.5824528336524963, 'pandas', 1), ('jazzband/tablib', 0.575348973274231, 'data', 0), ('eventual-inc/daft', 0.5715435743331909, 'pandas', 3), ('graphistry/pygraphistry', 0.5663058161735535, 'data', 1), ('rapidsai/cudf', 0.56623375415802, 'pandas', 2), ('holoviz/datashader', 0.5661620497703552, 'gis', 0), ('polyaxon/datatile', 0.5635982155799866, 'pandas', 1), ('apache/spark', 0.5628867149353027, 'data', 0), ('mito-ds/monorepo', 0.5610413551330566, 'jupyter', 1), ('contextlab/hypertools', 0.548748791217804, 'ml', 1), ('scikit-hep/awkward-1.0', 0.5477538704872131, 'data', 0), ('huggingface/datasets', 0.545326828956604, 'nlp', 1), ('jmcarpenter2/swifter', 0.5449737906455994, 'pandas', 0), ('dylanhogg/awesome-python', 0.5425941944122314, 'study', 2), ('jakevdp/pythondatasciencehandbook', 0.5422857999801636, 'study', 0), ('mljar/mljar-supervised', 0.5394431352615356, 'ml', 2), ('gradio-app/gradio', 0.5373766422271729, 'viz', 2), ('residentmario/geoplot', 0.5356773734092712, 'gis', 0), ('pandas-dev/pandas', 0.5320824980735779, 'pandas', 2), ('cython/cython', 0.5234464406967163, 'util', 0), ('holoviz/holoviz', 0.5222266316413879, 'viz', 0), ('kanaries/pygwalker', 0.5217480063438416, 'pandas', 2), ('bokeh/bokeh', 0.5212001800537109, 'viz', 1), ('fugue-project/fugue', 0.5170252919197083, 'pandas', 1), ('quantopian/qgrid', 0.5162068009376526, 'jupyter', 0), ('hi-primus/optimus', 0.5148595571517944, 'ml-ops', 3), ('wesm/pydata-book', 0.5123580098152161, 'study', 0), ('nalepae/pandarallel', 0.5113261938095093, 'pandas', 0), ('astanin/python-tabulate', 0.5106703042984009, 'util', 0), ('ploomber/ploomber', 0.5101336240768433, 'ml-ops', 2), ('ydataai/ydata-synthetic', 0.5099949836730957, 'data', 1), ('lux-org/lux', 0.5093144178390503, 'viz', 2), ('kubeflow-kale/kale', 0.5090245008468628, 'ml-ops', 1), ('holoviz/hvplot', 0.5080651640892029, 'pandas', 0), ('saulpw/visidata', 0.5060981512069702, 'term', 2), ('tobymao/sqlglot', 0.5004581809043884, 'data', 0), ('uber/petastorm', 0.500319242477417, 'data', 2), ('plotly/dash', 0.5000450611114502, 'viz', 1)] | 72 | 6 | null | 0.52 | 19 | 2 | 113 | 6 | 0 | 40 | 40 | 19 | 23 | 90 | 1.2 | 50 |
321 | web | https://github.com/graphql-python/graphene | [] | null | [] | [] | null | null | null | graphql-python/graphene | graphene | 7,884 | 871 | 141 | Python | http://graphene-python.org/ | GraphQL framework for Python | graphql-python | 2024-01-14 | 2015-09-24 | 435 | 18.094426 | https://avatars.githubusercontent.com/u/15002022?v=4 | GraphQL framework for Python | ['framework', 'graphene', 'graphql', 'relay'] | ['framework', 'graphene', 'graphql', 'relay'] | 2023-10-06 | [('pygraphviz/pygraphviz', 0.5481510162353516, 'viz', 0), ('accenture/cymple', 0.5159372091293335, 'data', 0), ('westhealth/pyvis', 0.5054649114608765, 'graph', 0), ('pydot/pydot', 0.5014999508857727, 'viz', 0)] | 207 | 6 | null | 0.37 | 15 | 7 | 101 | 3 | 2 | 7 | 2 | 15 | 15 | 90 | 1 | 50 |
154 | util | https://github.com/marshmallow-code/marshmallow | [] | null | [] | [] | null | null | null | marshmallow-code/marshmallow | marshmallow | 6,778 | 640 | 82 | Python | https://marshmallow.readthedocs.io/ | A lightweight library for converting complex objects to and from simple Python datatypes. | marshmallow-code | 2024-01-13 | 2013-11-10 | 533 | 12.709885 | https://avatars.githubusercontent.com/u/10334301?v=4 | A lightweight library for converting complex objects to and from simple Python datatypes. | ['deserialization', 'marshalling', 'schema', 'serde', 'serialization', 'validation'] | ['deserialization', 'marshalling', 'schema', 'serde', 'serialization', 'validation'] | 2024-01-10 | [('pyeve/cerberus', 0.6555448174476624, 'data', 0), ('python-odin/odin', 0.6529213190078735, 'util', 1), ('pylons/colander', 0.6454058885574341, 'util', 3), ('yukinarit/pyserde', 0.6370522379875183, 'util', 2), ('jsonpickle/jsonpickle', 0.6203814744949341, 'data', 2), ('pydantic/pydantic', 0.606956422328949, 'util', 2), ('instagram/libcst', 0.6035540699958801, 'util', 0), ('pytoolz/toolz', 0.5878369808197021, 'util', 0), ('uqfoundation/dill', 0.5810075402259827, 'data', 0), ('lk-geimfari/mimesis', 0.5606078505516052, 'data', 1), ('instagram/monkeytype', 0.5554887056350708, 'typing', 0), ('facebook/pyre-check', 0.5461035966873169, 'typing', 0), ('pyston/pyston', 0.5445635914802551, 'util', 0), ('brokenloop/jsontopydantic', 0.5406548380851746, 'util', 0), ('tiangolo/sqlmodel', 0.539881706237793, 'data', 0), ('pandas-dev/pandas', 0.5381511449813843, 'pandas', 0), ('lidatong/dataclasses-json', 0.5334739089012146, 'util', 0), ('samuelcolvin/rtoml', 0.5252963900566101, 'data', 1), ('fabiocaccamo/python-benedict', 0.5224064588546753, 'util', 0), ('konradhalas/dacite', 0.5163299441337585, 'util', 0), ('xrudelis/pytrait', 0.5146151185035706, 'util', 0), ('strawberry-graphql/strawberry', 0.5134101510047913, 'web', 0), ('fastai/fastcore', 0.5120862722396851, 'util', 0), ('google/pytype', 0.5046632289886475, 'typing', 0), ('imageio/imageio', 0.503285825252533, 'util', 0), ('kubeflow/fairing', 0.5004829168319702, 'ml-ops', 0)] | 208 | 1 | null | 2.23 | 43 | 33 | 124 | 0 | 0 | 17 | 17 | 43 | 30 | 90 | 0.7 | 50 |
508 | ml | https://github.com/google/automl | [] | null | [] | [] | null | null | null | google/automl | automl | 6,083 | 1,464 | 156 | Jupyter Notebook | null | Google Brain AutoML | google | 2024-01-13 | 2020-03-12 | 202 | 30.007752 | https://avatars.githubusercontent.com/u/1342004?v=4 | Google Brain AutoML | ['automl', 'efficientdet', 'efficientnet', 'efficientnetv2', 'object-detection'] | ['automl', 'efficientdet', 'efficientnet', 'efficientnetv2', 'object-detection'] | 2023-12-14 | [('rwightman/pytorch-image-models', 0.5850779414176941, 'ml-dl', 1), ('nvlabs/gcvit', 0.5431532859802246, 'diffusion', 1), ('keras-team/autokeras', 0.5386403799057007, 'ml-dl', 1), ('blakeblackshear/frigate', 0.5250091552734375, 'util', 1), ('deepmind/deepmind-research', 0.5214808583259583, 'ml', 0), ('deci-ai/super-gradients', 0.5212241411209106, 'ml-dl', 1), ('awslabs/autogluon', 0.5180317163467407, 'ml', 2), ('karpathy/micrograd', 0.5122846364974976, 'study', 0), ('googlecloudplatform/practical-ml-vision-book', 0.5014958381652832, 'study', 0)] | 42 | 4 | null | 0.38 | 14 | 6 | 47 | 1 | 0 | 1 | 1 | 14 | 11 | 90 | 0.8 | 50 |
300 | data | https://github.com/kaggle/kaggle-api | [] | null | [] | [] | null | null | null | kaggle/kaggle-api | kaggle-api | 5,734 | 1,050 | 194 | Python | null | Official Kaggle API | kaggle | 2024-01-14 | 2018-01-25 | 313 | 18.277778 | https://avatars.githubusercontent.com/u/1336944?v=4 | Official Kaggle API | [] | [] | 2024-01-08 | [] | 37 | 2 | null | 0.15 | 40 | 17 | 73 | 0 | 5 | 1 | 5 | 40 | 52 | 90 | 1.3 | 50 |
308 | crypto | https://github.com/crytic/slither | [] | null | [] | [] | null | null | null | crytic/slither | slither | 4,790 | 894 | 68 | Python | https://blog.trailofbits.com/2018/10/19/slither-a-solidity-static-analysis-framework/ | Static Analyzer for Solidity and Vyper | crytic | 2024-01-13 | 2018-09-05 | 281 | 16.994425 | https://avatars.githubusercontent.com/u/48330002?v=4 | Static Analyzer for Solidity and Vyper | ['ethereum', 'solidity', 'static-analysis', 'vyper'] | ['ethereum', 'solidity', 'static-analysis', 'vyper'] | 2023-10-18 | [('google/pytype', 0.6767980456352234, 'typing', 1), ('instagram/monkeytype', 0.5720254182815552, 'typing', 0), ('astral-sh/ruff', 0.5403991937637329, 'util', 1)] | 126 | 1 | null | 12.58 | 132 | 61 | 65 | 3 | 5 | 7 | 5 | 132 | 119 | 90 | 0.9 | 50 |
1,670 | testing | https://github.com/getsentry/responses | ['mocking', 'requests'] | null | [] | [] | null | null | null | getsentry/responses | responses | 3,994 | 339 | 91 | Python | null | A utility for mocking out the Python Requests library. | getsentry | 2024-01-13 | 2013-11-15 | 532 | 7.499464 | https://avatars.githubusercontent.com/u/1396951?v=4 | A utility for mocking out the Python Requests library. | ['tag-production'] | ['mocking', 'requests', 'tag-production'] | 2024-01-10 | [('jamielennox/requests-mock', 0.7649958729743958, 'testing', 1), ('requests/toolbelt', 0.7001904249191284, 'util', 0), ('wolever/parameterized', 0.6193545460700989, 'testing', 0), ('psf/requests', 0.6119535565376282, 'web', 1), ('lundberg/respx', 0.5848559737205505, 'testing', 1), ('nedbat/coveragepy', 0.5841493606567383, 'testing', 0), ('lk-geimfari/mimesis', 0.5823182463645935, 'data', 0), ('taverntesting/tavern', 0.5727768540382385, 'testing', 0), ('pytest-dev/pytest-mock', 0.5724997520446777, 'testing', 0), ('kevin1024/vcrpy', 0.5679908990859985, 'testing', 1), ('eleutherai/pyfra', 0.5386306047439575, 'ml', 0), ('buildbot/buildbot', 0.5227794647216797, 'util', 0), ('pytoolz/toolz', 0.52274489402771, 'util', 0), ('pympler/pympler', 0.520066499710083, 'perf', 0), ('pmorissette/bt', 0.5153909921646118, 'finance', 0), ('mementum/backtrader', 0.5119891166687012, 'finance', 0), ('ionelmc/pytest-benchmark', 0.5085520148277283, 'testing', 0), ('google/python-fire', 0.5072945952415466, 'term', 0), ('encode/httpx', 0.5034039616584778, 'web', 0), ('snyk/faker-security', 0.5025610327720642, 'security', 0), ('locustio/locust', 0.5024783611297607, 'testing', 0), ('mkdocstrings/griffe', 0.5013567209243774, 'util', 0)] | 124 | 5 | null | 0.92 | 27 | 23 | 124 | 0 | 6 | 5 | 6 | 27 | 60 | 90 | 2.2 | 50 |
320 | gui | https://github.com/r0x0r/pywebview | [] | null | [] | [] | null | null | null | r0x0r/pywebview | pywebview | 3,981 | 507 | 62 | Python | https://pywebview.flowrl.com | Build GUI for your Python program with JavaScript, HTML, and CSS | r0x0r | 2024-01-13 | 2014-11-20 | 479 | 8.29869 | null | Build GUI for your Python program with JavaScript, HTML, and CSS | ['cef', 'cocoa', 'gtk', 'gui', 'html', 'javascript', 'linux', 'osx', 'qt', 'webkit', 'windows'] | ['cef', 'cocoa', 'gtk', 'gui', 'html', 'javascript', 'linux', 'osx', 'qt', 'webkit', 'windows'] | 2023-12-10 | [('willmcgugan/textual', 0.6927530765533447, 'term', 0), ('parthjadhav/tkinter-designer', 0.6899272799491882, 'gui', 1), ('kivy/kivy', 0.6854493618011475, 'util', 2), ('hoffstadt/dearpygui', 0.6651965379714966, 'gui', 3), ('beeware/toga', 0.6624377965927124, 'gui', 1), ('pallets/flask', 0.6464259624481201, 'web', 0), ('pysimplegui/pysimplegui', 0.6416908502578735, 'gui', 2), ('webpy/webpy', 0.6379135251045227, 'web', 0), ('masoniteframework/masonite', 0.6317577362060547, 'web', 0), ('urwid/urwid', 0.6302554607391357, 'term', 0), ('reflex-dev/reflex', 0.6069856882095337, 'web', 0), ('plotly/dash', 0.6030216217041016, 'viz', 0), ('flet-dev/flet', 0.5987342000007629, 'web', 0), ('pyglet/pyglet', 0.593908965587616, 'gamedev', 0), ('pypa/build', 0.5934130549430847, 'util', 0), ('bokeh/bokeh', 0.5825904607772827, 'viz', 1), ('klen/muffin', 0.5798044800758362, 'web', 0), ('pypy/pypy', 0.5696408748626709, 'util', 0), ('wxwidgets/phoenix', 0.5664918422698975, 'gui', 3), ('bottlepy/bottle', 0.5649958252906799, 'web', 0), ('jquast/blessed', 0.5557140707969666, 'term', 0), ('reactive-python/reactpy', 0.5532059073448181, 'web', 1), ('holoviz/panel', 0.5402711629867554, 'viz', 1), ('microsoft/playwright-python', 0.5271952748298645, 'testing', 1), ('seleniumbase/seleniumbase', 0.5268258452415466, 'testing', 1), ('pyodide/pyodide', 0.5263348817825317, 'util', 0), ('vizzuhq/ipyvizzu', 0.5254994034767151, 'jupyter', 0), ('python/cpython', 0.5236340165138245, 'util', 0), ('dylanhogg/awesome-python', 0.5234578251838684, 'study', 0), ('pygamelib/pygamelib', 0.5211628079414368, 'gamedev', 0), ('voila-dashboards/voila', 0.5208461880683899, 'jupyter', 0), ('jupyterlab/jupyterlab-desktop', 0.520444393157959, 'jupyter', 0), ('pylons/pyramid', 0.5202688574790955, 'web', 0), ('maartenbreddels/ipyvolume', 0.5197725296020508, 'jupyter', 0), ('plotly/plotly.py', 0.5167466402053833, 'viz', 0), ('adafruit/circuitpython', 0.5162983536720276, 'util', 0), ('gradio-app/gradio', 0.5159224271774292, 'viz', 0), ('timofurrer/awesome-asyncio', 0.5146098136901855, 'study', 0), ('connorferster/handcalcs', 0.5135572552680969, 'jupyter', 0), ('1200wd/bitcoinlib', 0.5121883153915405, 'crypto', 0), ('cobrateam/splinter', 0.5121274590492249, 'testing', 0), ('pyscript/pyscript', 0.5112079381942749, 'web', 2), ('amaargiru/pyroad', 0.5109246969223022, 'study', 0), ('pallets/quart', 0.5082329511642456, 'web', 0), ('beeware/briefcase', 0.5074366927146912, 'util', 0), ('goldmansachs/gs-quant', 0.5072776079177856, 'finance', 0), ('clips/pattern', 0.5047861933708191, 'nlp', 0), ('jiffyclub/snakeviz', 0.5045416951179504, 'profiling', 0), ('dddomodossola/remi', 0.5024312138557434, 'gui', 1), ('eleutherai/pyfra', 0.5016577243804932, 'ml', 0)] | 123 | 2 | null | 3.92 | 72 | 56 | 111 | 1 | 13 | 7 | 13 | 72 | 148 | 90 | 2.1 | 50 |
100 | ml | https://github.com/skvark/opencv-python | [] | null | [] | [] | null | null | null | skvark/opencv-python | opencv-python | 3,923 | 812 | 86 | Python | https://pypi.org/project/opencv-python/ | Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages. | skvark | 2024-01-14 | 2016-04-08 | 407 | 9.625307 | https://avatars.githubusercontent.com/u/5009934?v=4 | Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages. | ['manylinux', 'opencv', 'opencv-contrib-python', 'opencv-python', 'precompiled', 'wheel'] | ['manylinux', 'opencv', 'opencv-contrib-python', 'opencv-python', 'precompiled', 'wheel'] | 2023-12-31 | [('pypa/pipenv', 0.5230939984321594, 'util', 0)] | 48 | 5 | null | 0.69 | 77 | 50 | 95 | 0 | 6 | 9 | 6 | 77 | 113 | 90 | 1.5 | 50 |
742 | diffusion | https://github.com/nateraw/stable-diffusion-videos | [] | null | [] | [] | null | null | null | nateraw/stable-diffusion-videos | stable-diffusion-videos | 3,917 | 373 | 53 | Python | null | Create 🔥 videos with Stable Diffusion by exploring the latent space and morphing between text prompts | nateraw | 2024-01-13 | 2022-09-06 | 73 | 53.657534 | null | Create 🔥 videos with Stable Diffusion by exploring the latent space and morphing between text prompts | ['ai-art', 'huggingface', 'huggingface-diffusers', 'machine-learning', 'stable-diffusion'] | ['ai-art', 'huggingface', 'huggingface-diffusers', 'machine-learning', 'stable-diffusion'] | 2023-05-07 | [('saharmor/dalle-playground', 0.671584963798523, 'diffusion', 2), ('compvis/stable-diffusion', 0.6355494856834412, 'diffusion', 0), ('carson-katri/dream-textures', 0.6127192378044128, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.6116631627082825, 'diffusion', 2), ('jina-ai/discoart', 0.5996933579444885, 'diffusion', 1), ('invoke-ai/invokeai', 0.5912322402000427, 'diffusion', 2), ('stability-ai/stability-sdk', 0.5674799680709839, 'diffusion', 2), ('albarji/mixture-of-diffusers', 0.5622400045394897, 'diffusion', 1), ('huggingface/diffusers', 0.5594053268432617, 'diffusion', 1), ('openai/glide-text2im', 0.5577892661094666, 'diffusion', 0), ('open-mmlab/mmediting', 0.5489683747291565, 'ml', 0), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5448333024978638, 'web', 0), ('chenyangqiqi/fatezero', 0.543769896030426, 'diffusion', 1), ('lunarring/latentblending', 0.5257456302642822, 'diffusion', 1), ('facebookresearch/mmf', 0.5128837823867798, 'ml-dl', 0), ('sharonzhou/long_stable_diffusion', 0.512672483921051, 'diffusion', 0), ('thereforegames/unprompted', 0.5079180002212524, 'diffusion', 2), ('thudm/cogvideo', 0.5062693357467651, 'ml', 0)] | 14 | 5 | null | 0.33 | 8 | 1 | 16 | 8 | 1 | 13 | 1 | 8 | 10 | 90 | 1.2 | 50 |
1,791 | util | https://github.com/bogdanp/dramatiq | [] | null | [] | [] | null | null | null | bogdanp/dramatiq | dramatiq | 3,884 | 277 | 63 | Python | https://dramatiq.io | A fast and reliable background task processing library for Python 3. | bogdanp | 2024-01-13 | 2017-05-30 | 348 | 11.16092 | null | A fast and reliable background task processing library for Python 3. | ['distributed-lock', 'rabbit', 'redis', 'task', 'task-manager', 'task-runner', 'task-scheduler'] | ['distributed-lock', 'rabbit', 'redis', 'task', 'task-manager', 'task-runner', 'task-scheduler'] | 2024-01-13 | [('agronholm/apscheduler', 0.6053295135498047, 'util', 0), ('samuelcolvin/arq', 0.5857503414154053, 'data', 1), ('mher/flower', 0.5586143732070923, 'perf', 1), ('joblib/loky', 0.5552358031272888, 'perf', 0), ('airtai/faststream', 0.5551525354385376, 'perf', 1), ('tox-dev/py-filelock', 0.5492278337478638, 'util', 0), ('python-trio/trio', 0.5322513580322266, 'perf', 0), ('dask/dask', 0.5178118944168091, 'perf', 0), ('sumerc/yappi', 0.517719566822052, 'profiling', 0), ('dask/distributed', 0.5118077993392944, 'perf', 0)] | 100 | 6 | null | 1.15 | 29 | 14 | 81 | 0 | 4 | 10 | 4 | 29 | 39 | 90 | 1.3 | 50 |
1,291 | llm | https://github.com/ravenscroftj/turbopilot | [] | null | [] | [] | null | null | null | ravenscroftj/turbopilot | turbopilot | 3,842 | 133 | 43 | C++ | null | Turbopilot is an open source large-language-model based code completion engine that runs locally on CPU | ravenscroftj | 2024-01-12 | 2023-04-09 | 42 | 90.858108 | null | Turbopilot is an open source large-language-model based code completion engine that runs locally on CPU | ['code-completion', 'cpp', 'language-model', 'machine-learning'] | ['code-completion', 'cpp', 'language-model', 'machine-learning'] | 2023-09-30 | [('databrickslabs/dolly', 0.5751809477806091, 'llm', 0), ('modularml/mojo', 0.5523777604103088, 'util', 1), ('salesforce/codegen', 0.5407807230949402, 'nlp', 0), ('lianjiatech/belle', 0.5382049083709717, 'llm', 0), ('microsoft/pycodegpt', 0.524189293384552, 'llm', 0), ('salesforce/codet5', 0.5119407773017883, 'nlp', 1), ('titanml/takeoff', 0.5088446140289307, 'llm', 1), ('togethercomputer/redpajama-data', 0.5085808038711548, 'llm', 0), ('thudm/codegeex', 0.5072124600410461, 'llm', 0), ('conceptofmind/toolformer', 0.5007780194282532, 'llm', 1)] | 7 | 1 | null | 5.13 | 1 | 1 | 9 | 4 | 7 | 11 | 7 | 1 | 0 | 90 | 0 | 50 |
889 | viz | https://github.com/has2k1/plotnine | [] | null | [] | [] | null | null | null | has2k1/plotnine | plotnine | 3,682 | 207 | 65 | Python | https://plotnine.org | A Grammar of Graphics for Python | has2k1 | 2024-01-13 | 2017-04-24 | 353 | 10.426375 | null | A Grammar of Graphics for Python | ['data-analysis', 'grammar', 'graphics', 'plotting'] | ['data-analysis', 'grammar', 'graphics', 'plotting'] | 2024-01-12 | [('altair-viz/altair', 0.6832043528556824, 'viz', 0), ('matplotlib/matplotlib', 0.6249548196792603, 'viz', 1), ('holoviz/holoviz', 0.6090349555015564, 'viz', 0), ('plotly/plotly.py', 0.6062517762184143, 'viz', 0), ('mwaskom/seaborn', 0.6059504747390747, 'viz', 0), ('holoviz/geoviews', 0.6033970713615417, 'gis', 1), ('residentmario/geoplot', 0.5918059349060059, 'gis', 0), ('vizzuhq/ipyvizzu', 0.585931122303009, 'jupyter', 1), ('scitools/cartopy', 0.5810568332672119, 'gis', 0), ('holoviz/hvplot', 0.5800595879554749, 'pandas', 1), ('imageio/imageio', 0.5798242092132568, 'util', 0), ('bokeh/bokeh', 0.5768696069717407, 'viz', 1), ('man-group/dtale', 0.5764876008033752, 'viz', 1), ('python/cpython', 0.5753418803215027, 'util', 0), ('pandas-dev/pandas', 0.5737428069114685, 'pandas', 1), ('scitools/iris', 0.5705260634422302, 'gis', 1), ('artelys/geonetworkx', 0.569690465927124, 'gis', 0), ('pytoolz/toolz', 0.5640243887901306, 'util', 0), ('opengeos/leafmap', 0.5541620254516602, 'gis', 0), ('holoviz/panel', 0.5538381338119507, 'viz', 0), ('pyston/pyston', 0.5506923198699951, 'util', 0), ('pysal/pysal', 0.5488908290863037, 'gis', 0), ('sympy/sympy', 0.5483768582344055, 'math', 0), ('graphistry/pygraphistry', 0.5480146408081055, 'data', 0), ('westhealth/pyvis', 0.5456839203834534, 'graph', 0), ('gboeing/pynamical', 0.5370927453041077, 'sim', 0), ('wesm/pydata-book', 0.5339842438697815, 'study', 0), ('cuemacro/chartpy', 0.5336882472038269, 'viz', 1), ('albahnsen/pycircular', 0.5327418446540833, 'math', 0), ('brandtbucher/specialist', 0.5308057069778442, 'perf', 0), ('enthought/mayavi', 0.5297136902809143, 'viz', 0), ('giswqs/geemap', 0.5272516012191772, 'gis', 0), ('nschloe/perfplot', 0.5270527005195618, 'perf', 0), ('pyparsing/pyparsing', 0.5245786905288696, 'util', 0), ('zulko/moviepy', 0.524414598941803, 'util', 0), ('jakevdp/pythondatasciencehandbook', 0.5220550894737244, 'study', 0), ('alexmojaki/heartrate', 0.5188327431678772, 'debug', 0), ('pygraphviz/pygraphviz', 0.5177363753318787, 'viz', 0), ('contextlab/hypertools', 0.5157314538955688, 'ml', 0), ('eleutherai/pyfra', 0.5156086683273315, 'ml', 0), ('dfki-ric/pytransform3d', 0.5153816342353821, 'math', 0), ('kanaries/pygwalker', 0.5138752460479736, 'pandas', 1), ('holoviz/holoviews', 0.5102058053016663, 'viz', 1), ('earthlab/earthpy', 0.5097348093986511, 'gis', 0), ('vispy/vispy', 0.5095175504684448, 'viz', 0), ('pyproj4/pyproj', 0.5081332921981812, 'gis', 0), ('federicoceratto/dashing', 0.5077523589134216, 'term', 0), ('rapidsai/cudf', 0.5067694783210754, 'pandas', 1), ('google/latexify_py', 0.5012533068656921, 'util', 0)] | 105 | 3 | null | 5.33 | 31 | 25 | 82 | 0 | 4 | 3 | 4 | 31 | 40 | 90 | 1.3 | 50 |
553 | jupyter | https://github.com/executablebooks/jupyter-book | [] | null | [] | [] | null | null | null | executablebooks/jupyter-book | jupyter-book | 3,592 | 649 | 63 | Python | http://jupyterbook.org | Create beautiful, publication-quality books and documents from computational content. | executablebooks | 2024-01-13 | 2018-06-14 | 293 | 12.229572 | https://avatars.githubusercontent.com/u/57655115?v=4 | Create beautiful, publication-quality books and documents from computational content. | ['documentation-generator', 'jupyter', 'sphinx-doc'] | ['documentation-generator', 'jupyter', 'sphinx-doc'] | 2023-12-05 | [('sphinx-doc/sphinx', 0.753897488117218, 'util', 0), ('mediawiki-client-tools/mediawiki-dump-generator', 0.535974383354187, 'data', 0), ('mitmproxy/pdoc', 0.5281922817230225, 'util', 1), ('squidfunk/mkdocs-material', 0.5147403478622437, 'util', 0)] | 134 | 5 | null | 0.81 | 66 | 24 | 68 | 1 | 4 | 8 | 4 | 66 | 96 | 90 | 1.5 | 50 |
1,603 | llm | https://github.com/1rgs/jsonformer | ['prompt-engineering', 'json'] | null | [] | [] | null | null | null | 1rgs/jsonformer | jsonformer | 3,448 | 109 | 19 | Jupyter Notebook | null | A Bulletproof Way to Generate Structured JSON from Language Models | 1rgs | 2024-01-13 | 2023-04-29 | 39 | 87.449275 | null | A Bulletproof Way to Generate Structured JSON from Language Models | [] | ['json', 'prompt-engineering'] | 2023-05-30 | [('neulab/prompt2model', 0.6683309674263, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5909636616706848, 'llm', 1), ('brokenloop/jsontopydantic', 0.5837192535400391, 'util', 0), ('guidance-ai/guidance', 0.5747532248497009, 'llm', 1), ('srush/minichain', 0.5634762644767761, 'llm', 1), ('hazyresearch/ama_prompting', 0.5634579062461853, 'llm', 1), ('juncongmoo/pyllama', 0.5465362071990967, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5433797240257263, 'llm', 0), ('lidatong/dataclasses-json', 0.529080331325531, 'util', 1), ('hannibal046/awesome-llm', 0.5063848495483398, 'study', 0), ('ai21labs/lm-evaluation', 0.5037403702735901, 'llm', 0)] | 6 | 4 | null | 0.69 | 8 | 0 | 9 | 8 | 0 | 0 | 0 | 8 | 8 | 90 | 1 | 50 |
1,564 | llm | https://github.com/deep-diver/llm-as-chatbot | ['chatbot'] | null | [] | [] | null | null | null | deep-diver/llm-as-chatbot | LLM-As-Chatbot | 3,161 | 392 | 50 | Python | null | LLM as a Chatbot Service | deep-diver | 2024-01-12 | 2023-02-27 | 48 | 65.658754 | null | LLM as a Chatbot Service | [] | ['chatbot'] | 2023-11-20 | [('nomic-ai/gpt4all', 0.7740846276283264, 'llm', 1), ('pathwaycom/llm-app', 0.6957004070281982, 'llm', 1), ('hwchase17/langchain', 0.6912153363227844, 'llm', 1), ('intel/intel-extension-for-transformers', 0.6634297966957092, 'perf', 1), ('embedchain/embedchain', 0.6422813534736633, 'llm', 0), ('deepset-ai/haystack', 0.6116864085197449, 'llm', 0), ('microsoft/promptcraft-robotics', 0.6085308194160461, 'sim', 0), ('chatarena/chatarena', 0.5871044397354126, 'llm', 0), ('thudm/chatglm2-6b', 0.5782683491706848, 'llm', 0), ('mmabrouk/chatgpt-wrapper', 0.5747789144515991, 'llm', 1), ('h2oai/h2o-llmstudio', 0.5731402635574341, 'llm', 1), ('shishirpatil/gorilla', 0.5617109537124634, 'llm', 0), ('aws-samples/serverless-pdf-chat', 0.5605927109718323, 'llm', 0), ('rcgai/simplyretrieve', 0.5600287914276123, 'llm', 0), ('run-llama/rags', 0.5595909953117371, 'llm', 1), ('gunthercox/chatterbot', 0.5590612888336182, 'nlp', 1), ('berriai/litellm', 0.5576227903366089, 'llm', 0), ('chainlit/chainlit', 0.548169732093811, 'llm', 0), ('microsoft/semantic-kernel', 0.5478009581565857, 'llm', 0), ('microsoft/autogen', 0.5456553101539612, 'llm', 1), ('togethercomputer/openchatkit', 0.5419533848762512, 'nlp', 1), ('ajndkr/lanarky', 0.5414519309997559, 'llm', 0), ('young-geng/easylm', 0.5367242097854614, 'llm', 1), ('mnotgod96/appagent', 0.5356595516204834, 'llm', 0), ('fasteval/fasteval', 0.5321249961853027, 'llm', 0), ('microsoft/promptflow', 0.5296043753623962, 'llm', 0), ('zilliztech/gptcache', 0.5247036218643188, 'llm', 1), ('microsoft/jarvis', 0.5197903513908386, 'llm', 0), ('bigscience-workshop/petals', 0.5034541487693787, 'data', 1), ('eugeneyan/open-llms', 0.502346932888031, 'study', 0), ('agenta-ai/agenta', 0.5022589564323425, 'llm', 0), ('larsbaunwall/bricky', 0.5015654563903809, 'llm', 0), ('errbotio/errbot', 0.5012304186820984, 'nlp', 1), ('nebuly-ai/nebullvm', 0.5001731514930725, 'perf', 0)] | 7 | 4 | null | 6.19 | 2 | 0 | 11 | 2 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 50 |
899 | ml | https://github.com/rucaibox/recbole | [] | null | [] | [] | null | null | null | rucaibox/recbole | RecBole | 3,022 | 562 | 40 | Python | https://recbole.io/ | A unified, comprehensive and efficient recommendation library | rucaibox | 2024-01-13 | 2020-06-11 | 189 | 15.929217 | https://avatars.githubusercontent.com/u/54706620?v=4 | A unified, comprehensive and efficient recommendation library | ['collaborative-filtering', 'ctr-prediction', 'deep-learning', 'graph-neural-networks', 'knowledge-graph', 'pytorch', 'recommendation-system', 'recommendations', 'recommender', 'recommender-systems', 'sequential-recommendation'] | ['collaborative-filtering', 'ctr-prediction', 'deep-learning', 'graph-neural-networks', 'knowledge-graph', 'pytorch', 'recommendation-system', 'recommendations', 'recommender', 'recommender-systems', 'sequential-recommendation'] | 2023-11-25 | [('pytorch/torchrec', 0.7371825575828552, 'ml-dl', 3), ('microsoft/recommenders', 0.5956623554229736, 'study', 3), ('nicolashug/surprise', 0.5934752821922302, 'ml', 1), ('pyg-team/pytorch_geometric', 0.566724419593811, 'ml-dl', 3), ('tensorlayer/tensorlayer', 0.5453563928604126, 'ml-rl', 1), ('a-r-j/graphein', 0.5301325917243958, 'sim', 3), ('explosion/thinc', 0.5200947523117065, 'ml-dl', 2)] | 70 | 5 | null | 2.96 | 86 | 35 | 44 | 2 | 1 | 2 | 1 | 86 | 136 | 90 | 1.6 | 50 |
822 | study | https://github.com/huggingface/diffusion-models-class | [] | null | [] | [] | null | null | null | huggingface/diffusion-models-class | diffusion-models-class | 2,931 | 309 | 71 | Jupyter Notebook | null | Materials for the Hugging Face Diffusion Models Course | huggingface | 2024-01-14 | 2022-10-13 | 67 | 43.28481 | https://avatars.githubusercontent.com/u/25720743?v=4 | Materials for the Hugging Face Diffusion Models Course | [] | [] | 2023-12-18 | [('huggingface/notebooks', 0.5564571619033813, 'ml', 0), ('huggingface/deep-rl-class', 0.551882803440094, 'study', 0)] | 20 | 5 | null | 0.62 | 10 | 7 | 15 | 1 | 0 | 0 | 0 | 10 | 9 | 90 | 0.9 | 50 |
1,362 | util | https://github.com/pyo3/maturin | ['rust'] | null | [] | [] | null | null | null | pyo3/maturin | maturin | 2,916 | 195 | 24 | Rust | https://maturin.rs | Build and publish crates with pyo3, rust-cpython and cffi bindings as well as rust binaries as python packages | pyo3 | 2024-01-14 | 2018-07-21 | 288 | 10.109955 | https://avatars.githubusercontent.com/u/28156855?v=4 | Build and publish crates with pyo3, rust-cpython and cffi bindings as well as rust binaries as python packages | ['cffi', 'cpython', 'cross-compile', 'manylinux', 'packaging', 'pyo3', 'pypy', 'rust-cpython', 'uniffi', 'wheels'] | ['cffi', 'cpython', 'cross-compile', 'manylinux', 'packaging', 'pyo3', 'pypy', 'rust', 'rust-cpython', 'uniffi', 'wheels'] | 2024-01-10 | [('pyo3/rust-numpy', 0.654156506061554, 'util', 1), ('pyo3/pyo3', 0.6540318131446838, 'util', 1), ('scikit-build/scikit-build', 0.6356386542320251, 'ml', 3), ('rustpython/rustpython', 0.6316028237342834, 'util', 1), ('pypa/installer', 0.6027328372001648, 'util', 0), ('pypy/pypy', 0.5836184024810791, 'util', 1), ('ofek/pyapp', 0.5643833875656128, 'util', 2), ('astral-sh/ruff', 0.556878387928009, 'util', 1), ('aswinnnn/pyscan', 0.552875280380249, 'security', 1), ('libtcod/python-tcod', 0.5518137812614441, 'gamedev', 1), ('eventual-inc/daft', 0.5351252555847168, 'pandas', 1), ('pyodide/micropip', 0.5349652171134949, 'util', 0), ('python/cpython', 0.5341805219650269, 'util', 1), ('delta-io/delta-rs', 0.532139241695404, 'pandas', 1), ('pdm-project/pdm', 0.5319200754165649, 'util', 1), ('pypa/hatch', 0.5255593061447144, 'util', 1), ('cython/cython', 0.5176377892494202, 'util', 1), ('pypa/virtualenv', 0.5136356353759766, 'util', 1), ('pytoolz/toolz', 0.5072370171546936, 'util', 0), ('pyodide/pyodide', 0.504960834980011, 'util', 1), ('pyo3/setuptools-rust', 0.5044507384300232, 'util', 1), ('ipython/ipython', 0.5035890936851501, 'util', 0)] | 102 | 1 | null | 7.62 | 121 | 103 | 67 | 0 | 29 | 52 | 29 | 121 | 171 | 90 | 1.4 | 50 |
1,294 | llm | https://github.com/microsoft/torchscale | [] | null | [] | [] | null | null | null | microsoft/torchscale | torchscale | 2,793 | 185 | 44 | Python | https://aka.ms/GeneralAI | Foundation Architecture for (M)LLMs | microsoft | 2024-01-14 | 2022-11-17 | 62 | 44.535308 | https://avatars.githubusercontent.com/u/6154722?v=4 | Foundation Architecture for (M)LLMs | ['computer-vision', 'machine-learning', 'multimodal', 'natural-language-processing', 'pretrained-language-model', 'speech-processing', 'transformer', 'translation'] | ['computer-vision', 'machine-learning', 'multimodal', 'natural-language-processing', 'pretrained-language-model', 'speech-processing', 'transformer', 'translation'] | 2023-12-27 | [('ludwig-ai/ludwig', 0.6496773958206177, 'ml-ops', 3), ('eugeneyan/open-llms', 0.633047878742218, 'study', 0), ('alpha-vllm/llama2-accessory', 0.602177619934082, 'llm', 0), ('bentoml/openllm', 0.5949733853340149, 'ml-ops', 0), ('iryna-kondr/scikit-llm', 0.5895041823387146, 'llm', 1), ('microsoft/lmops', 0.5832264423370361, 'llm', 0), ('vllm-project/vllm', 0.582655668258667, 'llm', 1), ('pathwaycom/llm-app', 0.5825396180152893, 'llm', 1), ('next-gpt/next-gpt', 0.582415759563446, 'llm', 1), ('salesforce/xgen', 0.5822688937187195, 'llm', 0), ('confident-ai/deepeval', 0.581218957901001, 'testing', 0), ('microsoft/jarvis', 0.5797202587127686, 'llm', 0), ('microsoft/semantic-kernel', 0.573233425617218, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5727461576461792, 'study', 1), ('intel/intel-extension-for-transformers', 0.570101261138916, 'perf', 0), ('ray-project/ray-llm', 0.5647855997085571, 'llm', 0), ('tigerlab-ai/tiger', 0.5640652179718018, 'llm', 0), ('explosion/spacy-llm', 0.5628166198730469, 'llm', 2), ('hiyouga/llama-factory', 0.5607731938362122, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5607730746269226, 'llm', 0), ('h2oai/h2o-llmstudio', 0.5583703517913818, 'llm', 0), ('optimalscale/lmflow', 0.5554569959640503, 'llm', 1), ('young-geng/easylm', 0.5538792610168457, 'llm', 2), ('bobazooba/xllm', 0.5502038598060608, 'llm', 0), ('deepset-ai/haystack', 0.546305775642395, 'llm', 1), ('microsoft/promptflow', 0.542982816696167, 'llm', 0), ('nebuly-ai/nebullvm', 0.5394699573516846, 'perf', 0), ('hwchase17/langchain', 0.5352845788002014, 'llm', 0), ('nat/openplayground', 0.5345559120178223, 'llm', 0), ('mlc-ai/mlc-llm', 0.532260000705719, 'llm', 0), ('bigscience-workshop/petals', 0.5316954255104065, 'data', 2), ('nvidia/tensorrt-llm', 0.5302456617355347, 'viz', 0), ('agenta-ai/agenta', 0.5262821912765503, 'llm', 0), ('argilla-io/argilla', 0.5222489237785339, 'nlp', 2), ('artidoro/qlora', 0.5133755207061768, 'llm', 0), ('juncongmoo/pyllama', 0.5133620500564575, 'llm', 0), ('tsinghuadatabasegroup/db-gpt', 0.5131277441978455, 'llm', 0), ('dylanhogg/llmgraph', 0.5129197835922241, 'ml', 0), ('lancedb/lancedb', 0.5128360390663147, 'data', 0), ('tensorflow/tensorflow', 0.5050406455993652, 'ml-dl', 1), ('llmware-ai/llmware', 0.5042523741722107, 'llm', 1)] | 16 | 3 | null | 1.04 | 32 | 19 | 14 | 1 | 0 | 0 | 0 | 32 | 38 | 90 | 1.2 | 50 |
544 | ml | https://github.com/lightly-ai/lightly | [] | null | [] | [] | null | null | null | lightly-ai/lightly | lightly | 2,654 | 233 | 26 | Python | https://docs.lightly.ai/self-supervised-learning/ | A python library for self-supervised learning on images. | lightly-ai | 2024-01-12 | 2020-10-13 | 172 | 15.430233 | https://avatars.githubusercontent.com/u/50146475?v=4 | A python library for self-supervised learning on images. | ['computer-vision', 'contrastive-learning', 'deep-learning', 'embeddings', 'machine-learning', 'pytorch', 'self-supervised-learning'] | ['computer-vision', 'contrastive-learning', 'deep-learning', 'embeddings', 'machine-learning', 'pytorch', 'self-supervised-learning'] | 2024-01-11 | [('mdbloice/augmentor', 0.7019970417022705, 'ml', 2), ('pytorch/ignite', 0.6327634453773499, 'ml-dl', 3), ('deci-ai/super-gradients', 0.610939621925354, 'ml-dl', 3), ('facebookresearch/vissl', 0.6104288101196289, 'ml', 0), ('rasbt/mlxtend', 0.6017403602600098, 'ml', 1), ('featurelabs/featuretools', 0.5864541530609131, 'ml', 1), ('facebookresearch/dinov2', 0.5861289501190186, 'diffusion', 0), ('kevinmusgrave/pytorch-metric-learning', 0.581474244594574, 'ml', 7), ('imageio/imageio', 0.5790013670921326, 'util', 0), ('skorch-dev/skorch', 0.5767190456390381, 'ml-dl', 2), ('intel/intel-extension-for-pytorch', 0.5627269148826599, 'perf', 3), ('google-research/deeplab2', 0.5617728233337402, 'ml', 0), ('weecology/deepforest', 0.5572022199630737, 'gis', 0), ('mrdbourke/pytorch-deep-learning', 0.5538014769554138, 'study', 3), ('pycaret/pycaret', 0.5519795417785645, 'ml', 1), ('ageron/handson-ml2', 0.5470166802406311, 'ml', 0), ('salesforce/blip', 0.5466323494911194, 'diffusion', 0), ('pytorch/rl', 0.5465163588523865, 'ml-rl', 2), ('xl0/lovely-tensors', 0.5458189845085144, 'ml-dl', 2), ('hysts/pytorch_image_classification', 0.5435891151428223, 'ml-dl', 2), ('python-pillow/pillow', 0.5426392555236816, 'util', 0), ('albumentations-team/albumentations', 0.5409029126167297, 'ml-dl', 2), ('kornia/kornia', 0.5407032370567322, 'ml-dl', 4), ('nvlabs/gcvit', 0.5394826531410217, 'diffusion', 1), ('pytorch/pytorch', 0.5388930439949036, 'ml-dl', 2), ('lucidrains/imagen-pytorch', 0.5377395153045654, 'ml-dl', 1), ('rasbt/machine-learning-book', 0.5357835292816162, 'study', 3), ('tensorflow/tensorflow', 0.5355916023254395, 'ml-dl', 2), ('cvxgrp/pymde', 0.5311744809150696, 'ml', 2), ('facebookresearch/pytorch3d', 0.5310624241828918, 'ml-dl', 0), ('ggerganov/ggml', 0.5310502052307129, 'ml', 1), ('merantix-momentum/squirrel-core', 0.5261020660400391, 'ml', 4), ('roboflow/supervision', 0.5256815552711487, 'ml', 4), ('oml-team/open-metric-learning', 0.5244993567466736, 'ml', 4), ('allenai/allennlp', 0.5244608521461487, 'nlp', 2), ('scikit-image/scikit-image', 0.5242587924003601, 'util', 1), ('karpathy/micrograd', 0.5221474766731262, 'study', 0), ('lutzroeder/netron', 0.5200084447860718, 'ml', 3), ('luispedro/mahotas', 0.5195291638374329, 'viz', 1), ('uber/petastorm', 0.5194175243377686, 'data', 3), ('tensorlayer/tensorlayer', 0.5181266665458679, 'ml-rl', 1), ('azavea/raster-vision', 0.5178630352020264, 'gis', 4), ('scikit-learn/scikit-learn', 0.5172454714775085, 'ml', 1), ('gradio-app/gradio', 0.5170907378196716, 'viz', 2), ('microsoft/semi-supervised-learning', 0.5161562561988831, 'ml', 4), ('pyg-team/pytorch_geometric', 0.5151110887527466, 'ml-dl', 2), ('huggingface/datasets', 0.5144714713096619, 'nlp', 4), ('xl0/lovely-numpy', 0.5120965242385864, 'util', 1), ('earthlab/earthpy', 0.5085079669952393, 'gis', 0), ('jeshraghian/snntorch', 0.5078932046890259, 'ml-dl', 2), ('aleju/imgaug', 0.5071452260017395, 'ml', 2), ('huggingface/huggingface_hub', 0.5056184530258179, 'ml', 3), ('ddbourgin/numpy-ml', 0.5032593607902527, 'ml', 1), ('aws/sagemaker-python-sdk', 0.5022170543670654, 'ml', 2), ('keras-team/autokeras', 0.5014397501945496, 'ml-dl', 2), ('tensorflow/data-validation', 0.5010249018669128, 'ml-ops', 0)] | 35 | 1 | null | 4.71 | 72 | 46 | 40 | 0 | 35 | 34 | 35 | 72 | 116 | 90 | 1.6 | 50 |
1,577 | ml | https://github.com/zjunlp/deepke | ['knowledge-graph'] | null | [] | [] | null | null | null | zjunlp/deepke | DeepKE | 2,653 | 601 | 44 | Python | http://deepke.zjukg.cn/ | An Open Toolkit for Knowledge Graph Extraction and Construction published at EMNLP2022 System Demonstrations. | zjunlp | 2024-01-14 | 2018-08-01 | 286 | 9.248506 | https://avatars.githubusercontent.com/u/41887875?v=4 | An Open Toolkit for Knowledge Graph Extraction and Construction published at EMNLP2022 System Demonstrations. | ['attribute-extraction', 'bert', 'chinese', 'deep-learning', 'deepke', 'document-level', 'few-shot', 'information-extraction', 'kg', 'knowledge-graph', 'knowprompt', 'lightner', 'low-resource', 'multi-modal', 'named-entity-recognition', 'ner', 'nlp', 'prompt', 'pytorch', 'relation-extraction'] | ['attribute-extraction', 'bert', 'chinese', 'deep-learning', 'deepke', 'document-level', 'few-shot', 'information-extraction', 'kg', 'knowledge-graph', 'knowprompt', 'lightner', 'low-resource', 'multi-modal', 'named-entity-recognition', 'ner', 'nlp', 'prompt', 'pytorch', 'relation-extraction'] | 2024-01-10 | [('accenture/ampligraph', 0.593433678150177, 'data', 1), ('dylanhogg/llmgraph', 0.5825223922729492, 'ml', 1), ('microsoft/vert-papers', 0.5728095769882202, 'nlp', 3), ('awslabs/dgl-ke', 0.5722960233688354, 'ml', 1), ('babelscape/rebel', 0.5535876154899597, 'nlp', 2), ('alibaba/easynlp', 0.5432515144348145, 'nlp', 4), ('deepgraphlearning/ultra', 0.5096688270568848, 'ml', 1)] | 31 | 1 | null | 11.06 | 54 | 53 | 66 | 0 | 5 | 3 | 5 | 54 | 190 | 90 | 3.5 | 50 |
1,755 | util | https://github.com/pantsbuild/pex | ['executable', 'venv'] | null | [] | [] | null | null | null | pantsbuild/pex | pex | 2,411 | 247 | 56 | Python | https://pex.readthedocs.io | A tool for generating .pex (Python EXecutable) files, lock files and venvs. | pantsbuild | 2024-01-13 | 2014-07-21 | 497 | 4.849713 | https://avatars.githubusercontent.com/u/3065172?v=4 | A tool for generating .pex (Python EXecutable) files, lock files and venvs. | [] | ['executable', 'venv'] | 2024-01-14 | [('pypa/virtualenv', 0.5755523443222046, 'util', 1), ('tox-dev/py-filelock', 0.5669477581977844, 'util', 0), ('pyenv/pyenv', 0.5503339171409607, 'util', 1), ('pypa/pipenv', 0.5425669550895691, 'util', 1), ('pypa/pipx', 0.5113623738288879, 'util', 1)] | 119 | 6 | null | 2.67 | 61 | 50 | 115 | 0 | 37 | 25 | 37 | 61 | 142 | 90 | 2.3 | 50 |
1,189 | diffusion | https://github.com/stability-ai/stability-sdk | [] | null | [] | [] | null | null | null | stability-ai/stability-sdk | stability-sdk | 2,381 | 336 | 60 | Jupyter Notebook | https://platform.stability.ai/ | SDK for interacting with stability.ai APIs (e.g. stable diffusion inference) | stability-ai | 2024-01-12 | 2022-08-22 | 75 | 31.686312 | https://avatars.githubusercontent.com/u/100950301?v=4 | SDK for interacting with stability.ai APIs (e.g. stable diffusion inference) | ['ai-art', 'generative-art', 'latent-diffusion', 'multimodal', 'stable-diffusion'] | ['ai-art', 'generative-art', 'latent-diffusion', 'multimodal', 'stable-diffusion'] | 2023-11-20 | [('carson-katri/dream-textures', 0.7153577208518982, 'diffusion', 1), ('bentoml/onediffusion', 0.6665179133415222, 'diffusion', 1), ('automatic1111/stable-diffusion-webui', 0.6460036039352417, 'diffusion', 2), ('comfyanonymous/comfyui', 0.6112304925918579, 'diffusion', 1), ('invoke-ai/invokeai', 0.5937549471855164, 'diffusion', 4), ('nateraw/stable-diffusion-videos', 0.5674799680709839, 'diffusion', 2), ('divamgupta/stable-diffusion-tensorflow', 0.5413241386413574, 'diffusion', 0), ('bentoml/bentoml', 0.5263645052909851, 'ml-ops', 0), ('jina-ai/jina', 0.524762749671936, 'ml', 1), ('thereforegames/unprompted', 0.5244125127792358, 'diffusion', 2), ('kubeflow/fairing', 0.5190809369087219, 'ml-ops', 0), ('google-research/torchsde', 0.5064745545387268, 'math', 0), ('activeloopai/deeplake', 0.5024917125701904, 'ml-ops', 0), ('huggingface/diffusers', 0.5022362470626831, 'diffusion', 1), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5016722679138184, 'web', 0)] | 16 | 2 | null | 0.42 | 15 | 6 | 17 | 2 | 11 | 18 | 11 | 15 | 28 | 90 | 1.9 | 50 |
195 | ml-rl | https://github.com/pettingzoo-team/pettingzoo | [] | null | [] | [] | null | null | null | pettingzoo-team/pettingzoo | PettingZoo | 2,196 | 360 | 20 | Python | https://pettingzoo.farama.org | An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities | pettingzoo-team | 2024-01-12 | 2020-01-20 | 210 | 10.450034 | https://avatars.githubusercontent.com/u/62961550?v=4 | An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities | ['api', 'gym', 'gymnasium', 'multi-agent-reinforcement-learning', 'multiagent-reinforcement-learning', 'reinforcement-learning'] | ['api', 'gym', 'gymnasium', 'multi-agent-reinforcement-learning', 'multiagent-reinforcement-learning', 'reinforcement-learning'] | 2024-01-11 | [('farama-foundation/gymnasium', 0.947864830493927, 'ml-rl', 3), ('nvidia-omniverse/isaacgymenvs', 0.6462188959121704, 'sim', 1), ('unity-technologies/ml-agents', 0.6170148849487305, 'ml-rl', 1), ('pytorch/rl', 0.6150112748146057, 'ml-rl', 2), ('deepmind/acme', 0.578000545501709, 'ml-rl', 1), ('facebookresearch/habitat-lab', 0.5756711363792419, 'sim', 1), ('google/dopamine', 0.5669017434120178, 'ml-rl', 0), ('inspirai/timechamber', 0.5665203332901001, 'sim', 1), ('thu-ml/tianshou', 0.556010365486145, 'ml-rl', 0), ('nvidia-omniverse/omniisaacgymenvs', 0.5554696917533875, 'sim', 0), ('tensorlayer/tensorlayer', 0.554624080657959, 'ml-rl', 1), ('humancompatibleai/imitation', 0.5525628924369812, 'ml-rl', 1), ('facebookresearch/reagent', 0.5440720915794373, 'ml-rl', 0), ('openai/baselines', 0.5400475859642029, 'ml-rl', 0), ('openai/gym', 0.5399578213691711, 'ml-rl', 1), ('deepmind/pysc2', 0.5372393131256104, 'ml-rl', 1), ('operand/agency', 0.5311383605003357, 'llm', 1), ('shangtongzhang/reinforcement-learning-an-introduction', 0.5307071805000305, 'study', 1), ('salesforce/warp-drive', 0.5245906710624695, 'ml-rl', 2), ('huggingface/deep-rl-class', 0.5236347317695618, 'study', 1), ('ai4finance-foundation/finrl', 0.5186222791671753, 'finance', 1), ('kzl/decision-transformer', 0.5139691829681396, 'ml-rl', 1), ('projectmesa/mesa', 0.5047821998596191, 'sim', 0), ('openai/spinningup', 0.5047026872634888, 'study', 0)] | 105 | 2 | null | 3.52 | 44 | 39 | 48 | 0 | 6 | 10 | 6 | 44 | 96 | 90 | 2.2 | 50 |
64 | sim | https://github.com/projectmesa/mesa | [] | null | [] | [] | null | null | null | projectmesa/mesa | mesa | 2,089 | 802 | 91 | Python | null | Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors. | projectmesa | 2024-01-14 | 2014-09-19 | 488 | 4.275731 | https://avatars.githubusercontent.com/u/8754505?v=4 | Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors. | ['agent-based-modeling', 'agent-based-simulation', 'complex-systems', 'complexity-analysis', 'gis', 'mesa', 'modeling-agents', 'simulation', 'simulation-environment', 'simulation-framework', 'spatial-models'] | ['agent-based-modeling', 'agent-based-simulation', 'complex-systems', 'complexity-analysis', 'gis', 'mesa', 'modeling-agents', 'simulation', 'simulation-environment', 'simulation-framework', 'spatial-models'] | 2024-01-13 | [('google-deepmind/concordia', 0.6392713785171509, 'sim', 1), ('zacwellmer/worldmodels', 0.5980151891708374, 'ml-rl', 1), ('ljvmiranda921/seagull', 0.5834044218063354, 'sim', 1), ('operand/agency', 0.5751315355300903, 'llm', 0), ('artemyk/dynpy', 0.5512898564338684, 'sim', 0), ('crowddynamics/crowddynamics', 0.5487060546875, 'sim', 0), ('pytorch/rl', 0.5338829159736633, 'ml-rl', 0), ('humanoidagents/humanoidagents', 0.5313592553138733, 'sim', 1), ('gboeing/pynamical', 0.530546247959137, 'sim', 0), ('pythonarcade/arcade', 0.5278759002685547, 'gamedev', 0), ('unity-technologies/ml-agents', 0.5214616060256958, 'ml-rl', 0), ('alephalpha/golly', 0.5125412344932556, 'sim', 0), ('scikit-mobility/scikit-mobility', 0.5076199769973755, 'gis', 1), ('lordmauve/pgzero', 0.5056121349334717, 'gamedev', 0), ('pettingzoo-team/pettingzoo', 0.5047821998596191, 'ml-rl', 0), ('transformeroptimus/superagi', 0.5027607083320618, 'llm', 0)] | 134 | 2 | null | 5.35 | 124 | 93 | 113 | 0 | 7 | 3 | 7 | 124 | 671 | 90 | 5.4 | 50 |
224 | sim | https://github.com/google/brax | [] | null | [] | [] | null | null | null | google/brax | brax | 1,940 | 219 | 39 | Jupyter Notebook | null | Massively parallel rigidbody physics simulation on accelerator hardware. | google | 2024-01-13 | 2021-06-02 | 138 | 13.971193 | https://avatars.githubusercontent.com/u/1342004?v=4 | Massively parallel rigidbody physics simulation on accelerator hardware. | ['jax', 'physics-simulation', 'reinforcement-learning', 'robotics'] | ['jax', 'physics-simulation', 'reinforcement-learning', 'robotics'] | 2024-01-03 | [('arise-initiative/robosuite', 0.5911247134208679, 'ml-rl', 3), ('deepmind/dm_control', 0.514367938041687, 'ml-rl', 2)] | 31 | 7 | null | 0.19 | 36 | 20 | 32 | 0 | 7 | 8 | 7 | 36 | 45 | 90 | 1.2 | 50 |
1,282 | viz | https://github.com/marcomusy/vedo | [] | null | [] | [] | null | null | null | marcomusy/vedo | vedo | 1,847 | 245 | 30 | Python | https://vedo.embl.es | A python module for scientific analysis of 3D data based on VTK and Numpy | marcomusy | 2024-01-12 | 2017-11-10 | 324 | 5.690581 | null | A python module for scientific analysis of 3D data based on VTK and Numpy | ['3d', '3d-graphics', 'dolfin', 'fenics', 'finite-elements', 'mesh', 'numpy', 'scientific-research', 'scientific-visualization', 'simulations', 'visualization', 'vtk'] | ['3d', '3d-graphics', 'dolfin', 'fenics', 'finite-elements', 'mesh', 'numpy', 'scientific-research', 'scientific-visualization', 'simulations', 'visualization', 'vtk'] | 2024-01-13 | [('enthought/mayavi', 0.742211639881134, 'viz', 2), ('pyvista/pyvista', 0.7296451330184937, 'viz', 6), ('pyqtgraph/pyqtgraph', 0.6459553241729736, 'viz', 3), ('contextlab/hypertools', 0.6358500719070435, 'ml', 1), ('numpy/numpy', 0.6334555745124817, 'math', 1), ('dfki-ric/pytransform3d', 0.6237989664077759, 'math', 1), ('scitools/iris', 0.6136019825935364, 'gis', 0), ('isl-org/open3d', 0.6037029027938843, 'sim', 2), ('matplotlib/matplotlib', 0.577059805393219, 'viz', 0), ('viblo/pymunk', 0.5741404294967651, 'sim', 0), ('maartenbreddels/ipyvolume', 0.5702053308486938, 'jupyter', 1), ('earthlab/earthpy', 0.5666988492012024, 'gis', 0), ('pysal/pysal', 0.560834527015686, 'gis', 0), ('altair-viz/altair', 0.5546357035636902, 'viz', 1), ('scikit-geometry/scikit-geometry', 0.5506011843681335, 'gis', 0), ('holoviz/holoviz', 0.5432279706001282, 'viz', 0), ('residentmario/geoplot', 0.5391072034835815, 'gis', 0), ('roban/cosmolopy', 0.537321925163269, 'sim', 0), ('mwaskom/seaborn', 0.5347036123275757, 'viz', 0), ('pokepetter/ursina', 0.5303018093109131, 'gamedev', 0), ('gboeing/pynamical', 0.5265044569969177, 'sim', 2), ('jakevdp/pythondatasciencehandbook', 0.5232061743736267, 'study', 1), ('cupy/cupy', 0.5182294845581055, 'math', 1), ('holoviz/hvplot', 0.5138098001480103, 'pandas', 0), ('eleutherai/pyfra', 0.5130149126052856, 'ml', 0), ('vispy/vispy', 0.5108841061592102, 'viz', 1), ('man-group/dtale', 0.5097540020942688, 'viz', 1), ('scipy/scipy', 0.5084977746009827, 'math', 0), ('wesm/pydata-book', 0.5054879784584045, 'study', 0), ('rasbt/mlxtend', 0.5048307180404663, 'ml', 0), ('albahnsen/pycircular', 0.5011388063430786, 'math', 0), ('makepath/xarray-spatial', 0.5006656646728516, 'gis', 0)] | 34 | 7 | null | 10.98 | 91 | 74 | 75 | 0 | 5 | 9 | 5 | 91 | 184 | 90 | 2 | 50 |
795 | util | https://github.com/open-telemetry/opentelemetry-python | [] | null | [] | [] | null | null | null | open-telemetry/opentelemetry-python | opentelemetry-python | 1,474 | 549 | 37 | Python | https://opentelemetry.io | OpenTelemetry Python API and SDK | open-telemetry | 2024-01-13 | 2019-05-07 | 247 | 5.967611 | https://avatars.githubusercontent.com/u/49998002?v=4 | OpenTelemetry Python API and SDK | ['correlationcontext', 'distributed-tracing', 'logging', 'metrics', 'opentelemetry', 'sdk', 'tracecontext'] | ['correlationcontext', 'distributed-tracing', 'logging', 'metrics', 'opentelemetry', 'sdk', 'tracecontext'] | 2024-01-05 | [('open-telemetry/opentelemetry-python-contrib', 0.7430706024169922, 'util', 0), ('openai/openai-python', 0.5268572568893433, 'util', 0), ('cohere-ai/cohere-python', 0.5118368864059448, 'util', 1), ('kubeflow/fairing', 0.5018121004104614, 'ml-ops', 0), ('gaogaotiantian/viztracer', 0.5011972784996033, 'profiling', 1)] | 209 | 8 | null | 2.73 | 184 | 71 | 57 | 0 | 7 | 12 | 7 | 184 | 250 | 90 | 1.4 | 50 |
1,392 | llm | https://github.com/explosion/spacy-llm | [] | null | [] | [] | null | null | null | explosion/spacy-llm | spacy-llm | 780 | 56 | 14 | Python | https://spacy.io/usage/large-language-models | 🦙 Integrating LLMs into structured NLP pipelines | explosion | 2024-01-13 | 2023-03-16 | 45 | 17.0625 | https://avatars.githubusercontent.com/u/20011530?v=4 | 🦙 Integrating LLMs into structured NLP pipelines | ['anthropic', 'claude', 'cohere', 'dolly', 'falcon', 'gpt-3', 'gpt-4', 'large-language-models', 'llama', 'llm', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'nlp', 'openai', 'prompt-engineering', 'spacy', 'text-classification'] | ['anthropic', 'claude', 'cohere', 'dolly', 'falcon', 'gpt-3', 'gpt-4', 'large-language-models', 'llama', 'llm', 'machine-learning', 'named-entity-recognition', 'natural-language-processing', 'nlp', 'openai', 'prompt-engineering', 'spacy', 'text-classification'] | 2023-12-28 | [('paddlepaddle/paddlenlp', 0.691947877407074, 'llm', 3), ('argilla-io/argilla', 0.6727258563041687, 'nlp', 5), ('llmware-ai/llmware', 0.6662053465843201, 'llm', 3), ('mooler0410/llmspracticalguide', 0.6550344228744507, 'study', 3), ('infinitylogesh/mutate', 0.6443644762039185, 'nlp', 0), ('salesforce/xgen', 0.6350138187408447, 'llm', 3), ('lianjiatech/belle', 0.6267397999763489, 'llm', 1), ('bobazooba/xllm', 0.6183709502220154, 'llm', 5), ('nltk/nltk', 0.6157956123352051, 'nlp', 3), ('young-geng/easylm', 0.6080474257469177, 'llm', 3), ('flairnlp/flair', 0.6013752222061157, 'nlp', 4), ('deepset-ai/haystack', 0.5950032472610474, 'llm', 4), ('confident-ai/deepeval', 0.5939695239067078, 'testing', 1), ('norskregnesentral/skweak', 0.5937286615371704, 'nlp', 2), ('cg123/mergekit', 0.5918619632720947, 'llm', 2), ('explosion/spacy-models', 0.5904589295387268, 'nlp', 4), ('dylanhogg/llmgraph', 0.5829962491989136, 'ml', 1), ('pathwaycom/llm-app', 0.5805977582931519, 'llm', 2), ('vllm-project/vllm', 0.5805611610412598, 'llm', 2), ('keras-team/keras-nlp', 0.5786953568458557, 'nlp', 3), ('eleutherai/the-pile', 0.5771530270576477, 'data', 1), ('microsoft/autogen', 0.573653519153595, 'llm', 1), ('explosion/spacy', 0.573280930519104, 'nlp', 6), ('huggingface/text-generation-inference', 0.5718468427658081, 'llm', 2), ('aiwaves-cn/agents', 0.5685261487960815, 'nlp', 1), ('nomic-ai/gpt4all', 0.5681854486465454, 'llm', 0), ('juncongmoo/pyllama', 0.5681570768356323, 'llm', 0), ('neuml/txtai', 0.5675748586654663, 'nlp', 4), ('ray-project/ray-llm', 0.5664099454879761, 'llm', 2), ('rasahq/rasa', 0.5661826729774475, 'llm', 4), ('microsoft/torchscale', 0.5628166198730469, 'llm', 2), ('night-chen/toolqa', 0.5622978806495667, 'llm', 1), ('lexpredict/lexpredict-lexnlp', 0.5600504875183105, 'nlp', 1), ('hwchase17/langchain', 0.5581352710723877, 'llm', 0), ('explosion/spacy-stanza', 0.5577221512794495, 'nlp', 4), ('allenai/allennlp', 0.5512605905532837, 'nlp', 2), ('iryna-kondr/scikit-llm', 0.550988495349884, 'llm', 2), ('databrickslabs/dolly', 0.5508483648300171, 'llm', 1), ('bigscience-workshop/petals', 0.5496975779533386, 'data', 5), ('microsoft/lmops', 0.5478001832962036, 'llm', 2), ('alibaba/easynlp', 0.5462448596954346, 'nlp', 3), ('makcedward/nlpaug', 0.5450884103775024, 'nlp', 3), ('thudm/chatglm2-6b', 0.5399860739707947, 'llm', 2), ('microsoft/unilm', 0.5390931367874146, 'nlp', 2), ('whitead/paper-qa', 0.5373473763465881, 'llm', 1), ('squeezeailab/squeezellm', 0.5361400246620178, 'llm', 4), ('tigerlab-ai/tiger', 0.5356959104537964, 'llm', 2), ('nebuly-ai/nebullvm', 0.5306288003921509, 'perf', 2), ('epfllm/meditron', 0.5306103825569153, 'llm', 0), ('deepset-ai/farm', 0.5298573970794678, 'nlp', 1), ('agenta-ai/agenta', 0.5294641256332397, 'llm', 3), ('iclrandd/blackstone', 0.5293763279914856, 'nlp', 1), ('jonasgeiping/cramming', 0.526769757270813, 'nlp', 1), ('hiyouga/llama-efficient-tuning', 0.5260066986083984, 'llm', 3), ('hiyouga/llama-factory', 0.5260065793991089, 'llm', 3), ('jina-ai/thinkgpt', 0.5256912708282471, 'llm', 0), ('sloria/textblob', 0.5245431065559387, 'nlp', 2), ('explosion/spacy-transformers', 0.524055004119873, 'llm', 5), ('lm-sys/fastchat', 0.5232176780700684, 'llm', 0), ('eugeneyan/open-llms', 0.5212215185165405, 'study', 2), ('zilliztech/gptcache', 0.5211718082427979, 'llm', 4), ('ctlllll/llm-toolmaker', 0.520075261592865, 'llm', 0), ('huggingface/transformers', 0.519777774810791, 'nlp', 3), ('guardrails-ai/guardrails', 0.5183983445167542, 'llm', 3), ('run-llama/rags', 0.5166354775428772, 'llm', 2), ('princeton-nlp/alce', 0.5158350467681885, 'llm', 0), ('hannibal046/awesome-llm', 0.5151101350784302, 'study', 0), ('freedomintelligence/llmzoo', 0.5140294432640076, 'llm', 0), ('ibm/dromedary', 0.5127615928649902, 'llm', 0), ('ibm/transition-amr-parser', 0.5116180777549744, 'nlp', 2), ('next-gpt/next-gpt', 0.5109996199607849, 'llm', 3), ('bigscience-workshop/biomedical', 0.510345458984375, 'data', 0), ('jbesomi/texthero', 0.5079323053359985, 'nlp', 2), ('bentoml/openllm', 0.5071743726730347, 'ml-ops', 3), ('maartengr/bertopic', 0.507010817527771, 'nlp', 2), ('openlmlab/moss', 0.5049393773078918, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.504305899143219, 'study', 2), ('franck-dernoncourt/neuroner', 0.5040908455848694, 'nlp', 3), ('ludwig-ai/ludwig', 0.5036592483520508, 'ml-ops', 4), ('eth-sri/lmql', 0.502147376537323, 'llm', 1), ('thilinarajapakse/simpletransformers', 0.5020976662635803, 'nlp', 2), ('mlc-ai/web-llm', 0.5018036961555481, 'llm', 1), ('intel/intel-extension-for-transformers', 0.5016040205955505, 'perf', 0), ('qanastek/drbert', 0.5015144348144531, 'llm', 2)] | 16 | 5 | null | 5.15 | 68 | 54 | 10 | 1 | 23 | 28 | 23 | 68 | 87 | 90 | 1.3 | 50 |
1,852 | util | https://github.com/lastmile-ai/aiconfig | ['config', 'llm'] | AIConfig saves prompts, models and model parameters as source control friendly configs. This allows you to iterate on prompts and model parameters separately from your application code. | [] | [] | 1 | null | null | lastmile-ai/aiconfig | aiconfig | 600 | 35 | 7 | Python | https://aiconfig.lastmileai.dev | aiconfig -- config-driven, source control friendly AI application development | lastmile-ai | 2024-01-12 | 2023-09-01 | 21 | 27.81457 | https://avatars.githubusercontent.com/u/123273171?v=4 | aiconfig -- config-driven, source control friendly AI application development | ['ai', 'developer-tools', 'generative-ai', 'llm', 'llm-ops'] | ['ai', 'config', 'developer-tools', 'generative-ai', 'llm', 'llm-ops'] | 2024-01-14 | [('microsoft/promptflow', 0.7025976181030273, 'llm', 2), ('antonosika/gpt-engineer', 0.6765998005867004, 'llm', 1), ('prefecthq/marvin', 0.6677407622337341, 'nlp', 2), ('bentoml/bentoml', 0.6600256562232971, 'ml-ops', 2), ('microsoft/lmops', 0.6332518458366394, 'llm', 1), ('cheshire-cat-ai/core', 0.6183017492294312, 'llm', 2), ('avaiga/taipy', 0.6097587943077087, 'data', 1), ('sweepai/sweep', 0.6068457365036011, 'llm', 3), ('pythagora-io/gpt-pilot', 0.5966111421585083, 'llm', 2), ('pathwaycom/llm-app', 0.5902220606803894, 'llm', 1), ('mlc-ai/mlc-llm', 0.588663637638092, 'llm', 1), ('microsoft/semantic-kernel', 0.5658584237098694, 'llm', 2), ('mindsdb/mindsdb', 0.5651490092277527, 'data', 2), ('transformeroptimus/superagi', 0.5631660223007202, 'llm', 2), ('ludwig-ai/ludwig', 0.5598220229148865, 'ml-ops', 1), ('operand/agency', 0.5538642406463623, 'llm', 2), ('smol-ai/developer', 0.5501940846443176, 'llm', 1), ('zenml-io/zenml', 0.5441376566886902, 'ml-ops', 2), ('netflix/metaflow', 0.5414328575134277, 'ml-ops', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5404054522514343, 'study', 1), ('microsoft/generative-ai-for-beginners', 0.5391389727592468, 'study', 2), ('h2oai/h2o-llmstudio', 0.5382325053215027, 'llm', 3), ('activeloopai/deeplake', 0.5284830331802368, 'ml-ops', 2), ('tigerlab-ai/tiger', 0.5260499715805054, 'llm', 1), ('allegroai/clearml', 0.5247846841812134, 'ml-ops', 1), ('arize-ai/phoenix', 0.5230908393859863, 'ml-interpretability', 0), ('iterative/dvc', 0.522969663143158, 'ml-ops', 2), ('giskard-ai/giskard', 0.5168980360031128, 'data', 0), ('pytorchlightning/pytorch-lightning', 0.5128691792488098, 'ml-dl', 1), ('embedchain/embedchain', 0.5092721581459045, 'llm', 2), ('lucidrains/toolformer-pytorch', 0.5029549598693848, 'llm', 0), ('polyaxon/polyaxon', 0.5021164417266846, 'ml-ops', 0), ('jina-ai/jina', 0.5008789300918579, 'ml', 1), ('salesforce/codet5', 0.500541627407074, 'nlp', 0)] | 15 | 1 | null | 5.6 | 900 | 762 | 4 | 0 | 5 | 18 | 5 | 900 | 610 | 90 | 0.7 | 50 |
1,305 | study | https://github.com/christoschristofidis/awesome-deep-learning | ['awesome'] | null | [] | [] | null | null | null | christoschristofidis/awesome-deep-learning | awesome-deep-learning | 22,183 | 5,979 | 1,213 | null | null | A curated list of awesome Deep Learning tutorials, projects and communities. | christoschristofidis | 2024-01-13 | 2015-01-02 | 473 | 46.841931 | null | A curated list of awesome Deep Learning tutorials, projects and communities. | ['awesome', 'awesome-list', 'deep-learning', 'deep-learning-tutorial', 'deep-networks', 'face-images', 'machine-learning', 'neural-network', 'recurrent-networks'] | ['awesome', 'awesome-list', 'deep-learning', 'deep-learning-tutorial', 'deep-networks', 'face-images', 'machine-learning', 'neural-network', 'recurrent-networks'] | 2022-11-14 | [('dylanhogg/awesome-python', 0.6466501355171204, 'study', 4), ('mrdbourke/pytorch-deep-learning', 0.5786244869232178, 'study', 2), ('tensorflow/tensorflow', 0.5731549263000488, 'ml-dl', 3), ('rasbt/deeplearning-models', 0.5625115036964417, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.55058354139328, 'ml-dl', 1), ('graykode/nlp-tutorial', 0.5480766296386719, 'study', 0), ('lutzroeder/netron', 0.5479288101196289, 'ml', 3), ('dylanhogg/crazy-awesome-crypto', 0.5464283227920532, 'crypto', 2), ('d2l-ai/d2l-en', 0.5462198853492737, 'study', 2), ('huggingface/transformers', 0.5454069972038269, 'nlp', 2), ('explosion/thinc', 0.5450423359870911, 'ml-dl', 2), ('udacity/deep-learning-v2-pytorch', 0.5436199903488159, 'study', 3), ('keras-team/keras', 0.5434898138046265, 'ml-dl', 2), ('deepfakes/faceswap', 0.5433439612388611, 'ml-dl', 2), ('timofurrer/awesome-asyncio', 0.5383524298667908, 'study', 2), ('roboflow/notebooks', 0.5378063917160034, 'study', 2), ('mosaicml/composer', 0.5376695990562439, 'ml-dl', 3), ('nyandwi/modernconvnets', 0.5358662605285645, 'ml-dl', 0), ('pytorch/ignite', 0.5337355136871338, 'ml-dl', 3), ('amanchadha/coursera-deep-learning-specialization', 0.5327649116516113, 'study', 2), ('luodian/otter', 0.5302937030792236, 'llm', 2), ('rasbt/machine-learning-book', 0.5299058556556702, 'study', 2), ('tensorlayer/tensorlayer', 0.5272840857505798, 'ml-rl', 2), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5266667008399963, 'study', 2), ('ashleve/lightning-hydra-template', 0.52616947889328, 'util', 1), ('paddlepaddle/paddlenlp', 0.5255681872367859, 'llm', 0), ('rwightman/pytorch-image-models', 0.5237160921096802, 'ml-dl', 0), ('neuralmagic/sparseml', 0.5221423506736755, 'ml-dl', 0), ('aiqc/aiqc', 0.5192874670028687, 'ml-ops', 0), ('mrdbourke/tensorflow-deep-learning', 0.5140354037284851, 'study', 1), ('horovod/horovod', 0.5127199292182922, 'ml-ops', 2), ('chandlerbang/awesome-self-supervised-gnn', 0.5087785124778748, 'study', 3), ('iperov/deepfacelab', 0.5062230825424194, 'ml-dl', 2), ('rasbt/stat453-deep-learning-ss20', 0.506165087223053, 'study', 0), ('gradio-app/gradio', 0.5050585269927979, 'viz', 2), ('aistream-peelout/flow-forecast', 0.5039076209068298, 'time-series', 1), ('huggingface/autotrain-advanced', 0.5007786750793457, 'ml', 2), ('deci-ai/super-gradients', 0.5002260804176331, 'ml-dl', 2)] | 147 | 7 | null | 0 | 3 | 1 | 110 | 14 | 0 | 0 | 0 | 3 | 0 | 90 | 0 | 49 |
241 | ml | https://github.com/deepmind/deepmind-research | [] | null | [] | [] | null | null | null | deepmind/deepmind-research | deepmind-research | 12,418 | 2,508 | 337 | Jupyter Notebook | null | This repository contains implementations and illustrative code to accompany DeepMind publications | deepmind | 2024-01-13 | 2019-01-15 | 263 | 47.21673 | https://avatars.githubusercontent.com/u/8596759?v=4 | This repository contains implementations and illustrative code to accompany DeepMind publications | [] | [] | 2023-06-02 | [('rasbt/machine-learning-book', 0.5797885060310364, 'study', 0), ('nvidia/deeplearningexamples', 0.5370939373970032, 'ml-dl', 0), ('deepmind/dm_control', 0.5342397689819336, 'ml-rl', 0), ('microsoft/deepspeed', 0.5327295660972595, 'ml-dl', 0), ('bigcode-project/starcoder', 0.5220165252685547, 'llm', 0), ('google/automl', 0.5214808583259583, 'ml', 0), ('google-research/deeplab2', 0.5203995704650879, 'ml', 0), ('iperov/deepfacelab', 0.5150367021560669, 'ml-dl', 0), ('allenai/allennlp', 0.514882504940033, 'nlp', 0), ('microsoft/semi-supervised-learning', 0.5121714472770691, 'ml', 0), ('lucidrains/imagen-pytorch', 0.5075194835662842, 'ml-dl', 0), ('pytorch/fairseq', 0.5041647553443909, 'nlp', 0), ('graykode/nlp-tutorial', 0.5026014447212219, 'study', 0)] | 92 | 2 | null | 0.27 | 34 | 9 | 61 | 8 | 0 | 0 | 0 | 34 | 17 | 90 | 0.5 | 49 |
167 | sim | https://github.com/bulletphysics/bullet3 | [] | null | [] | [] | null | null | null | bulletphysics/bullet3 | bullet3 | 11,488 | 2,812 | 404 | C++ | http://bulletphysics.org | Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc. | bulletphysics | 2024-01-14 | 2011-04-12 | 668 | 17.197605 | https://avatars.githubusercontent.com/u/6955508?v=4 | Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc. | ['computer-animation', 'game-development', 'kinematics', 'pybullet', 'reinforcement-learning', 'robotics', 'simulation', 'simulator', 'virtual-reality'] | ['computer-animation', 'game-development', 'kinematics', 'pybullet', 'reinforcement-learning', 'robotics', 'simulation', 'simulator', 'virtual-reality'] | 2023-11-28 | [('viblo/pymunk', 0.5624234080314636, 'sim', 0), ('tensorlayer/tensorlayer', 0.5209137201309204, 'ml-rl', 1), ('arise-initiative/robosuite', 0.5200599431991577, 'ml-rl', 2), ('aimhubio/aim', 0.5082801580429077, 'ml-ops', 0), ('pytorch/rl', 0.505810022354126, 'ml-rl', 2), ('facebookresearch/habitat-lab', 0.5023512840270996, 'sim', 3)] | 305 | 3 | null | 0.08 | 47 | 7 | 155 | 2 | 0 | 2 | 2 | 47 | 38 | 90 | 0.8 | 49 |
649 | util | https://github.com/magicstack/uvloop | [] | null | [] | [] | null | null | null | magicstack/uvloop | uvloop | 9,768 | 572 | 226 | Cython | null | Ultra fast asyncio event loop. | magicstack | 2024-01-13 | 2015-11-08 | 429 | 22.754077 | https://avatars.githubusercontent.com/u/14324950?v=4 | Ultra fast asyncio event loop. | ['async', 'async-await', 'async-python', 'asyncio', 'event-loop', 'high-performance', 'libuv', 'networking'] | ['async', 'async-await', 'async-python', 'asyncio', 'event-loop', 'high-performance', 'libuv', 'networking'] | 2023-10-22 | [('agronholm/anyio', 0.7205407619476318, 'perf', 2), ('tiangolo/asyncer', 0.6868960857391357, 'perf', 2), ('aio-libs/aiohttp', 0.6718955636024475, 'web', 2), ('python-trio/trio', 0.6619237065315247, 'perf', 3), ('erdewit/nest_asyncio', 0.6544142365455627, 'util', 2), ('alex-sherman/unsync', 0.6438055634498596, 'util', 0), ('samuelcolvin/arq', 0.6330302953720093, 'data', 2), ('timofurrer/awesome-asyncio', 0.6274605989456177, 'study', 1), ('miguelgrinberg/python-socketio', 0.6088827252388, 'util', 1), ('sumerc/yappi', 0.6066219806671143, 'profiling', 1), ('airtai/faststream', 0.6053869128227234, 'perf', 1), ('pallets/quart', 0.5947157740592957, 'web', 1), ('alirn76/panther', 0.5940344929695129, 'web', 0), ('neoteroi/blacksheep', 0.5796182155609131, 'web', 1), ('noxdafox/pebble', 0.5719602108001709, 'perf', 1), ('pytest-dev/pytest-asyncio', 0.552464485168457, 'testing', 1), ('geeogi/async-python-lambda-template', 0.5491867065429688, 'template', 0), ('samuelcolvin/aioaws', 0.5443870425224304, 'data', 1), ('encode/httpx', 0.5275475978851318, 'web', 1), ('gbeced/basana', 0.5239098072052002, 'finance', 1), ('samuelcolvin/watchfiles', 0.5178032517433167, 'util', 1), ('fastai/fastcore', 0.5093868970870972, 'util', 0), ('tiangolo/fastapi', 0.5084176659584045, 'web', 2), ('huge-success/sanic', 0.5082210898399353, 'web', 1), ('encode/starlette', 0.5062094926834106, 'web', 1), ('klen/py-frameworks-bench', 0.5054547190666199, 'perf', 0)] | 60 | 3 | null | 0.21 | 24 | 11 | 100 | 3 | 2 | 10 | 2 | 24 | 25 | 90 | 1 | 49 |
1,194 | llm | https://github.com/thudm/glm-130b | [] | null | [] | [] | null | null | null | thudm/glm-130b | GLM-130B | 7,447 | 602 | 96 | Python | null | GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023) | thudm | 2024-01-13 | 2022-08-03 | 77 | 95.649541 | https://avatars.githubusercontent.com/u/48590610?v=4 | GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023) | [] | [] | 2023-07-25 | [('openai/finetune-transformer-lm', 0.590366780757904, 'llm', 0), ('thudm/chatglm-6b', 0.5821515321731567, 'llm', 0), ('microsoft/unilm', 0.5527222156524658, 'nlp', 0), ('cg123/mergekit', 0.542129635810852, 'llm', 0), ('yizhongw/self-instruct', 0.5371110439300537, 'llm', 0), ('salesforce/blip', 0.5314812064170837, 'diffusion', 0), ('qanastek/drbert', 0.5260924696922302, 'llm', 0), ('thudm/chatglm2-6b', 0.5260385274887085, 'llm', 0), ('ofa-sys/ofa', 0.5175337195396423, 'llm', 0), ('freedomintelligence/llmzoo', 0.5044916272163391, 'llm', 0), ('thudm/codegeex', 0.5032495856285095, 'llm', 0), ('lm-sys/fastchat', 0.5007140636444092, 'llm', 0)] | 6 | 2 | null | 0.33 | 8 | 1 | 18 | 6 | 0 | 0 | 0 | 8 | 4 | 90 | 0.5 | 49 |
1,275 | nlp | https://github.com/deeppavlov/deeppavlov | [] | null | [] | [] | null | null | null | deeppavlov/deeppavlov | DeepPavlov | 6,439 | 1,135 | 209 | Python | https://deeppavlov.ai | An open source library for deep learning end-to-end dialog systems and chatbots. | deeppavlov | 2024-01-13 | 2017-11-17 | 323 | 19.899779 | https://avatars.githubusercontent.com/u/29918795?v=4 | An open source library for deep learning end-to-end dialog systems and chatbots. | ['ai', 'artificial-intelligence', 'bot', 'chatbot', 'chitchat', 'deep-learning', 'deep-neural-networks', 'dialogue-agents', 'dialogue-manager', 'dialogue-systems', 'entity-extraction', 'intent-classification', 'intent-detection', 'machine-learning', 'named-entity-recognition', 'nlp', 'nlp-machine-learning', 'question-answering', 'slot-filling', 'tensorflow'] | ['ai', 'artificial-intelligence', 'bot', 'chatbot', 'chitchat', 'deep-learning', 'deep-neural-networks', 'dialogue-agents', 'dialogue-manager', 'dialogue-systems', 'entity-extraction', 'intent-classification', 'intent-detection', 'machine-learning', 'named-entity-recognition', 'nlp', 'nlp-machine-learning', 'question-answering', 'slot-filling', 'tensorflow'] | 2023-12-27 | [('rasahq/rasa', 0.7903884053230286, 'llm', 4), ('nvidia/nemo', 0.7471092939376831, 'nlp', 3), ('openlmlab/moss', 0.6669769287109375, 'llm', 2), ('krohling/bondai', 0.6658996939659119, 'llm', 0), ('togethercomputer/openchatkit', 0.6648150086402893, 'nlp', 1), ('rcgai/simplyretrieve', 0.6620361804962158, 'llm', 3), ('embedchain/embedchain', 0.6491185426712036, 'llm', 1), ('nomic-ai/gpt4all', 0.6342305541038513, 'llm', 1), ('gunthercox/chatterbot', 0.6284914016723633, 'nlp', 3), ('facebookresearch/parlai', 0.6255822777748108, 'nlp', 0), ('gunthercox/chatterbot-corpus', 0.6254500150680542, 'nlp', 0), ('allenai/allennlp', 0.6174921989440918, 'nlp', 2), ('tensorlayer/tensorlayer', 0.6134109497070312, 'ml-rl', 4), ('lm-sys/fastchat', 0.606029212474823, 'llm', 1), ('databrickslabs/dolly', 0.5981650352478027, 'llm', 1), ('cheshire-cat-ai/core', 0.5946456789970398, 'llm', 2), ('explosion/thinc', 0.5923045873641968, 'ml-dl', 6), ('thilinarajapakse/simpletransformers', 0.5851011276245117, 'nlp', 2), ('nvidia/deeplearningexamples', 0.5808957815170288, 'ml-dl', 3), ('larsbaunwall/bricky', 0.5805360078811646, 'llm', 1), ('paddlepaddle/paddlenlp', 0.5695449113845825, 'llm', 2), ('deepset-ai/haystack', 0.5695079565048218, 'llm', 4), ('franck-dernoncourt/neuroner', 0.5672761797904968, 'nlp', 5), ('prefecthq/marvin', 0.5636438131332397, 'nlp', 1), ('laion-ai/open-assistant', 0.563031792640686, 'llm', 2), ('minimaxir/simpleaichat', 0.559428870677948, 'llm', 1), ('espnet/espnet', 0.5584684610366821, 'nlp', 1), ('tensorflow/tensorflow', 0.5553365349769592, 'ml-dl', 4), ('dialogflow/dialogflow-python-client-v2', 0.5524868369102478, 'nlp', 1), ('huggingface/transformers', 0.5488126873970032, 'nlp', 4), ('blinkdl/chatrwkv', 0.5481441617012024, 'llm', 1), ('llmware-ai/llmware', 0.5467641353607178, 'llm', 4), ('fasteval/fasteval', 0.5457442402839661, 'llm', 0), ('keras-team/keras-nlp', 0.5425511002540588, 'nlp', 4), ('lucidrains/toolformer-pytorch', 0.5414809584617615, 'llm', 2), ('google-research/language', 0.5389516949653625, 'nlp', 1), ('run-llama/rags', 0.5382777452468872, 'llm', 1), ('graykode/nlp-tutorial', 0.529963493347168, 'study', 2), ('tensorflow/tensor2tensor', 0.5281538963317871, 'ml', 2), ('aiwaves-cn/agents', 0.5271100997924805, 'nlp', 0), ('alibaba/easynlp', 0.52702796459198, 'nlp', 3), ('facebookresearch/habitat-lab', 0.5260887145996094, 'sim', 2), ('langchain-ai/chat-langchain', 0.5239380598068237, 'llm', 1), ('argilla-io/argilla', 0.5165128111839294, 'nlp', 3), ('chatarena/chatarena', 0.5164510011672974, 'llm', 2), ('unity-technologies/ml-agents', 0.5153900980949402, 'ml-rl', 2), ('microsoft/autogen', 0.5114785432815552, 'llm', 1), ('uberi/speech_recognition', 0.509485125541687, 'ml', 0), ('pathwaycom/llm-app', 0.5093848705291748, 'llm', 2), ('neuml/txtai', 0.5076900720596313, 'nlp', 2), ('microsoft/generative-ai-for-beginners', 0.5074864625930786, 'study', 1), ('lupantech/chameleon-llm', 0.5067449808120728, 'llm', 1), ('jina-ai/clip-as-service', 0.5053911805152893, 'nlp', 1), ('explosion/spacy', 0.5052632689476013, 'nlp', 6), ('speechbrain/speechbrain', 0.5031290054321289, 'nlp', 1), ('mlc-ai/web-llm', 0.5006988644599915, 'llm', 1)] | 76 | 3 | null | 0.62 | 37 | 14 | 75 | 1 | 6 | 10 | 6 | 37 | 9 | 90 | 0.2 | 49 |
1,822 | nlp | https://github.com/facebookresearch/metaseq | ['fairseq'] | A codebase for working with Open Pre-trained Transformers, originally forked from fairseq. | [] | [] | null | null | null | facebookresearch/metaseq | metaseq | 6,297 | 711 | 110 | Python | null | Repo for external large-scale work | facebookresearch | 2024-01-13 | 2022-05-02 | 91 | 69.089342 | https://avatars.githubusercontent.com/u/16943930?v=4 | Repo for external large-scale work | [] | ['fairseq'] | 2023-06-08 | [] | 54 | 3 | null | 0.9 | 2 | 0 | 21 | 7 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 49 |
282 | util | https://github.com/sdispater/pendulum | [] | null | [] | [] | null | null | null | sdispater/pendulum | pendulum | 5,900 | 398 | 69 | Python | https://pendulum.eustace.io | Python datetimes made easy | sdispater | 2024-01-14 | 2016-06-27 | 396 | 14.893617 | null | Python datetimes made easy | ['date', 'datetime', 'time', 'timezones'] | ['date', 'datetime', 'time', 'timezones'] | 2023-12-16 | [('dateutil/dateutil', 0.7966391444206238, 'util', 3), ('arrow-py/arrow', 0.7535154819488525, 'util', 4), ('scrapinghub/dateparser', 0.6702179908752441, 'util', 2), ('stub42/pytz', 0.646413266658783, 'util', 0), ('spulec/freezegun', 0.54648357629776, 'testing', 0), ('rjt1990/pyflux', 0.5265621542930603, 'time-series', 0)] | 96 | 3 | null | 0.9 | 67 | 45 | 92 | 1 | 2 | 7 | 2 | 67 | 82 | 90 | 1.2 | 49 |
1,015 | util | https://github.com/wireservice/csvkit | [] | null | [] | [] | null | null | null | wireservice/csvkit | csvkit | 5,701 | 601 | 130 | Python | https://csvkit.readthedocs.io | A suite of utilities for converting to and working with CSV, the king of tabular file formats. | wireservice | 2024-01-13 | 2011-04-01 | 669 | 8.514402 | https://avatars.githubusercontent.com/u/17111824?v=4 | A suite of utilities for converting to and working with CSV, the king of tabular file formats. | [] | [] | 2023-12-21 | [('jazzband/prettytable', 0.5902390480041504, 'term', 0), ('jazzband/tablib', 0.5830636620521545, 'data', 0), ('saulpw/visidata', 0.5774484276771545, 'term', 0), ('camelot-dev/camelot', 0.567770779132843, 'util', 0), ('crunch-io/lazycsv', 0.561709463596344, 'perf', 0), ('astanin/python-tabulate', 0.5541922450065613, 'util', 0), ('dask/fastparquet', 0.5032108426094055, 'data', 0)] | 107 | 9 | null | 1.9 | 102 | 69 | 156 | 1 | 0 | 2 | 2 | 102 | 80 | 90 | 0.8 | 49 |
542 | ml-dl | https://github.com/facebookresearch/mmf | [] | null | [] | [] | null | null | null | facebookresearch/mmf | mmf | 5,352 | 928 | 117 | Python | https://mmf.sh/ | A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) | facebookresearch | 2024-01-13 | 2018-06-27 | 291 | 18.337739 | https://avatars.githubusercontent.com/u/16943930?v=4 | A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) | ['captioning', 'deep-learning', 'dialog', 'hateful-memes', 'multi-tasking', 'multimodal', 'pretrained-models', 'pytorch', 'textvqa', 'vqa'] | ['captioning', 'deep-learning', 'dialog', 'hateful-memes', 'multi-tasking', 'multimodal', 'pretrained-models', 'pytorch', 'textvqa', 'vqa'] | 2024-01-03 | [('pytorch/fairseq', 0.6375062465667725, 'nlp', 1), ('microsoft/i-code', 0.5485939383506775, 'ml', 0), ('rasahq/rasa', 0.5447145104408264, 'llm', 0), ('nvlabs/prismer', 0.5333836674690247, 'diffusion', 1), ('nvidia/nemo', 0.5225995779037476, 'nlp', 1), ('jina-ai/clip-as-service', 0.52168208360672, 'nlp', 2), ('fourthbrain/fastapi-for-machine-learning-live-demo', 0.5202192068099976, 'web', 0), ('docarray/docarray', 0.516020655632019, 'data', 3), ('nateraw/stable-diffusion-videos', 0.5128837823867798, 'diffusion', 0), ('luodian/otter', 0.5072057843208313, 'llm', 1), ('ofa-sys/ofa', 0.5046265721321106, 'llm', 2), ('salesforce/blip', 0.5001189112663269, 'diffusion', 0)] | 116 | 5 | null | 0.38 | 7 | 4 | 68 | 0 | 0 | 1 | 1 | 7 | 6 | 90 | 0.9 | 49 |
1,887 | util | https://github.com/rsalmei/alive-progress | ['progress-bar', 'cli'] | null | [] | [] | 1 | null | null | rsalmei/alive-progress | alive-progress | 4,854 | 191 | 49 | Python | null | A new kind of Progress Bar, with real-time throughput, ETA, and very cool animations! | rsalmei | 2024-01-14 | 2019-08-05 | 234 | 20.730933 | null | A new kind of Progress Bar, with real-time throughput, ETA, and very cool animations! | ['alive', 'animated', 'animations', 'bar', 'cli', 'eta', 'feedback', 'monitoring', 'multi-threaded', 'progress', 'progress-bar', 'progressbar', 'repl', 'spinner', 'spinner-styles', 'spinners', 'terminal', 'throughput', 'visual'] | ['alive', 'animated', 'animations', 'bar', 'cli', 'eta', 'feedback', 'monitoring', 'multi-threaded', 'progress', 'progress-bar', 'progressbar', 'repl', 'spinner', 'spinner-styles', 'spinners', 'terminal', 'throughput', 'visual'] | 2023-12-02 | [('tqdm/tqdm', 0.6888198256492615, 'term', 5), ('wolph/python-progressbar', 0.6114795207977295, 'util', 7), ('rockhopper-technologies/enlighten', 0.5524495244026184, 'term', 0)] | 7 | 3 | null | 0.83 | 9 | 5 | 54 | 1 | 0 | 1 | 1 | 9 | 31 | 90 | 3.4 | 49 |
27 | typing | https://github.com/google/pytype | ['code-quality'] | null | [] | [] | null | null | null | google/pytype | pytype | 4,452 | 279 | 57 | Python | https://google.github.io/pytype | A static type analyzer for Python code | google | 2024-01-13 | 2015-03-18 | 462 | 9.618519 | https://avatars.githubusercontent.com/u/1342004?v=4 | A static type analyzer for Python code | ['linter', 'static-analysis', 'static-code-analysis', 'typechecker', 'types', 'typing'] | ['code-quality', 'linter', 'static-analysis', 'static-code-analysis', 'typechecker', 'types', 'typing'] | 2024-01-11 | [('microsoft/pyright', 0.8127192258834839, 'typing', 2), ('instagram/monkeytype', 0.8035555481910706, 'typing', 1), ('facebook/pyre-check', 0.7848848104476929, 'typing', 3), ('python/mypy', 0.7456424236297607, 'typing', 5), ('agronholm/typeguard', 0.7289842367172241, 'typing', 2), ('python/typeshed', 0.7091848254203796, 'typing', 3), ('astral-sh/ruff', 0.6849864721298218, 'util', 4), ('crytic/slither', 0.6767980456352234, 'crypto', 1), ('rubik/radon', 0.6680740714073181, 'util', 1), ('landscapeio/prospector', 0.6452606916427612, 'util', 0), ('grantjenks/blue', 0.6101509928703308, 'util', 1), ('psf/black', 0.5877479314804077, 'util', 1), ('pytoolz/toolz', 0.5861064195632935, 'util', 0), ('pycqa/mccabe', 0.5844917893409729, 'util', 0), ('nedbat/coveragepy', 0.5753951072692871, 'testing', 0), ('klen/pylama', 0.5735052824020386, 'util', 1), ('pyutils/line_profiler', 0.5699858069419861, 'profiling', 0), ('pycqa/pylint', 0.5689358115196228, 'util', 4), ('google/yapf', 0.5599178075790405, 'util', 1), ('pycqa/flake8', 0.5498300194740295, 'util', 4), ('xrudelis/pytrait', 0.5462278127670288, 'util', 0), ('tiangolo/typer', 0.5461122393608093, 'term', 0), ('eugeneyan/python-collab-template', 0.5415253043174744, 'template', 0), ('pycqa/pylint-django', 0.5338144898414612, 'util', 1), ('pympler/pympler', 0.524192750453949, 'perf', 0), ('patrick-kidger/torchtyping', 0.5230032205581665, 'typing', 1), ('benfred/py-spy', 0.5136662721633911, 'profiling', 0), ('gaogaotiantian/viztracer', 0.5134180784225464, 'profiling', 0), ('python-rope/rope', 0.5130378603935242, 'util', 0), ('mynameisfiber/high_performance_python_2e', 0.5118113160133362, 'study', 0), ('pycqa/isort', 0.5101639032363892, 'util', 2), ('pycqa/pyflakes', 0.5093093514442444, 'util', 1), ('pandas-dev/pandas', 0.5089789628982544, 'pandas', 0), ('pythonspeed/filprofiler', 0.5083953738212585, 'profiling', 0), ('marshmallow-code/marshmallow', 0.5046632289886475, 'util', 0), ('alexmojaki/birdseye', 0.5039346218109131, 'debug', 0), ('ta-lib/ta-lib-python', 0.5031558275222778, 'finance', 0), ('aswinnnn/pyscan', 0.5005221366882324, 'security', 1)] | 100 | 2 | null | 10.46 | 57 | 44 | 107 | 0 | 0 | 22 | 22 | 57 | 27 | 90 | 0.5 | 49 |
209 | web | https://github.com/pywebio/pywebio | [] | null | [] | [] | null | null | null | pywebio/pywebio | PyWebIO | 4,234 | 363 | 54 | Python | https://pywebio.readthedocs.io | Write interactive web app in script way. | pywebio | 2024-01-13 | 2020-02-29 | 204 | 20.711391 | https://avatars.githubusercontent.com/u/83836839?v=4 | Write interactive web app in script way. | ['pywebio'] | ['pywebio'] | 2023-12-12 | [('webpy/webpy', 0.5835192799568176, 'web', 0), ('pyscript/pyscript', 0.5674092173576355, 'web', 0), ('reflex-dev/reflex', 0.5351123213768005, 'web', 0), ('masoniteframework/masonite', 0.5325572490692139, 'web', 0), ('pallets/flask', 0.5090134143829346, 'web', 0)] | 19 | 3 | null | 0.75 | 12 | 7 | 47 | 1 | 0 | 8 | 8 | 12 | 14 | 90 | 1.2 | 49 |
938 | llm | https://github.com/microsoft/biogpt | [] | null | [] | [] | null | null | null | microsoft/biogpt | BioGPT | 4,169 | 436 | 68 | Python | null | null | microsoft | 2024-01-12 | 2022-08-15 | 76 | 54.752345 | https://avatars.githubusercontent.com/u/6154722?v=4 | microsoft/BioGPT | [] | [] | 2023-11-13 | [] | 9 | 4 | null | 0.42 | 10 | 2 | 17 | 2 | 0 | 0 | 0 | 10 | 8 | 90 | 0.8 | 49 |
1,598 | nlp | https://github.com/thilinarajapakse/simpletransformers | [] | null | [] | [] | null | null | null | thilinarajapakse/simpletransformers | simpletransformers | 3,901 | 723 | 62 | Python | https://simpletransformers.ai/ | Transformers for Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI | thilinarajapakse | 2024-01-14 | 2019-10-04 | 225 | 17.293857 | null | Transformers for Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI | ['conversational-ai', 'named-entity-recognition', 'question-answering', 'text-classification', 'transformers'] | ['conversational-ai', 'named-entity-recognition', 'question-answering', 'text-classification', 'transformers'] | 2023-12-18 | [('huggingface/transformers', 0.713013768196106, 'nlp', 0), ('cdpierse/transformers-interpret', 0.6818839311599731, 'ml-interpretability', 1), ('nvidia/nemo', 0.6740487813949585, 'nlp', 0), ('keras-team/keras-nlp', 0.6119193434715271, 'nlp', 0), ('llmware-ai/llmware', 0.6119190454483032, 'llm', 2), ('microsoft/generative-ai-for-beginners', 0.6100896596908569, 'study', 1), ('lucidrains/toolformer-pytorch', 0.6100559234619141, 'llm', 1), ('deepset-ai/haystack', 0.5894189476966858, 'llm', 2), ('prefecthq/marvin', 0.5893362760543823, 'nlp', 0), ('deeppavlov/deeppavlov', 0.5851011276245117, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.5818993449211121, 'llm', 2), ('explosion/thinc', 0.5814999938011169, 'ml-dl', 0), ('docarray/docarray', 0.5803972482681274, 'data', 0), ('rasahq/rasa', 0.5802013874053955, 'llm', 1), ('bentoml/bentoml', 0.5780894160270691, 'ml-ops', 0), ('intellabs/fastrag', 0.5657897591590881, 'nlp', 2), ('explosion/spacy', 0.5573221445083618, 'nlp', 2), ('cheshire-cat-ai/core', 0.5490924715995789, 'llm', 0), ('lvwerra/trl', 0.5451595783233643, 'llm', 0), ('lm-sys/fastchat', 0.5447478890419006, 'llm', 0), ('sloria/textblob', 0.5410082936286926, 'nlp', 0), ('nvlabs/prismer', 0.5408389568328857, 'diffusion', 0), ('krohling/bondai', 0.534867525100708, 'llm', 0), ('makcedward/nlpaug', 0.5320999026298523, 'nlp', 0), ('ddbourgin/numpy-ml', 0.5308157801628113, 'ml', 0), ('graykode/nlp-tutorial', 0.5299626588821411, 'study', 0), ('deepset-ai/farm', 0.5268033742904663, 'nlp', 1), ('microsoft/lmops', 0.5261474251747131, 'llm', 0), ('explosion/spacy-transformers', 0.5258124470710754, 'llm', 0), ('nltk/nltk', 0.5254354476928711, 'nlp', 0), ('explosion/spacy-models', 0.5240768790245056, 'nlp', 0), ('milvus-io/bootcamp', 0.5199248790740967, 'data', 1), ('google/trax', 0.5190243721008301, 'ml-dl', 0), ('embedchain/embedchain', 0.518301248550415, 'llm', 0), ('rcgai/simplyretrieve', 0.5177233815193176, 'llm', 0), ('huggingface/autotrain-advanced', 0.517383873462677, 'ml', 0), ('extreme-bert/extreme-bert', 0.5170273184776306, 'llm', 0), ('mindsdb/mindsdb', 0.515864372253418, 'data', 0), ('alibaba/easynlp', 0.5156533122062683, 'nlp', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5155435800552368, 'study', 0), ('amanchadha/coursera-deep-learning-specialization', 0.5146978497505188, 'study', 0), ('eleutherai/knowledge-neurons', 0.5122392177581787, 'ml-interpretability', 1), ('bigscience-workshop/megatron-deepspeed', 0.5100935697555542, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5100935697555542, 'llm', 0), ('alignmentresearch/tuned-lens', 0.5096346735954285, 'ml-interpretability', 1), ('eugeneyan/obsidian-copilot', 0.5094960927963257, 'llm', 0), ('databrickslabs/dolly', 0.507858157157898, 'llm', 0), ('ofa-sys/ofa', 0.5072410702705383, 'llm', 0), ('franck-dernoncourt/neuroner', 0.5060073733329773, 'nlp', 1), ('jina-ai/finetuner', 0.5025129914283752, 'ml', 0), ('explosion/spacy-llm', 0.5020976662635803, 'llm', 2), ('modularml/mojo', 0.5019367933273315, 'util', 0), ('noahshinn/reflexion', 0.5012626051902771, 'llm', 0), ('espnet/espnet', 0.5008228421211243, 'nlp', 0), ('promptslab/awesome-prompt-engineering', 0.5007800459861755, 'study', 0), ('next-gpt/next-gpt', 0.5003290772438049, 'llm', 0)] | 100 | 5 | null | 0.25 | 10 | 3 | 52 | 1 | 0 | 62 | 62 | 10 | 7 | 90 | 0.7 | 49 |
1,386 | security | https://github.com/rhinosecuritylabs/pacu | [] | null | [] | [] | null | null | null | rhinosecuritylabs/pacu | pacu | 3,872 | 640 | 111 | Python | https://rhinosecuritylabs.com/aws/pacu-open-source-aws-exploitation-framework/ | The AWS exploitation framework, designed for testing the security of Amazon Web Services environments. | rhinosecuritylabs | 2024-01-13 | 2018-06-13 | 293 | 13.176471 | https://avatars.githubusercontent.com/u/11430746?v=4 | The AWS exploitation framework, designed for testing the security of Amazon Web Services environments. | ['aws', 'aws-security', 'penetration-testing', 'security'] | ['aws', 'aws-security', 'penetration-testing', 'security'] | 2024-01-09 | [('flipkart-incubator/astra', 0.5590912699699402, 'web', 2), ('newsapps/beeswithmachineguns', 0.5492108464241028, 'testing', 0), ('swisskyrepo/payloadsallthethings', 0.5338615775108337, 'security', 2), ('prefecthq/prefect-aws', 0.5137910842895508, 'data', 1), ('aws-samples/sagemaker-ssh-helper', 0.5007947087287903, 'util', 1)] | 53 | 3 | null | 1.79 | 30 | 26 | 68 | 0 | 11 | 4 | 11 | 30 | 12 | 90 | 0.4 | 49 |
797 | util | https://github.com/miguelgrinberg/python-socketio | [] | null | [] | [] | null | null | null | miguelgrinberg/python-socketio | python-socketio | 3,638 | 597 | 60 | Python | null | Python Socket.IO server and client | miguelgrinberg | 2024-01-13 | 2015-07-15 | 445 | 8.159564 | null | Python Socket.IO server and client | ['asyncio', 'eventlet', 'gevent', 'long-polling', 'low-latency', 'socket-io', 'socketio', 'socketio-server', 'web-server', 'websocket'] | ['asyncio', 'eventlet', 'gevent', 'long-polling', 'low-latency', 'socket-io', 'socketio', 'socketio-server', 'web-server', 'websocket'] | 2024-01-11 | [('websocket-client/websocket-client', 0.7025880217552185, 'web', 1), ('aio-libs/aiohttp', 0.6701440215110779, 'web', 1), ('encode/starlette', 0.6212427616119385, 'web', 0), ('magicstack/uvloop', 0.6088827252388, 'util', 1), ('encode/httpx', 0.5959307551383972, 'web', 1), ('bmoscon/cryptofeed', 0.595906138420105, 'crypto', 2), ('encode/uvicorn', 0.5958155989646912, 'web', 1), ('airtai/faststream', 0.5774848461151123, 'perf', 1), ('pallets/quart', 0.5742831826210022, 'web', 1), ('python-trio/trio', 0.5562794208526611, 'perf', 0), ('sumerc/yappi', 0.552412211894989, 'profiling', 2), ('neoteroi/blacksheep', 0.5502622127532959, 'web', 1), ('agronholm/anyio', 0.5345661640167236, 'perf', 1)] | 69 | 4 | null | 1.38 | 59 | 55 | 104 | 0 | 4 | 11 | 4 | 59 | 97 | 90 | 1.6 | 49 |
426 | util | https://github.com/pycqa/flake8 | ['code-quality'] | null | [] | [] | null | null | null | pycqa/flake8 | flake8 | 3,144 | 303 | 37 | Python | https://flake8.pycqa.org | flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code. | pycqa | 2024-01-14 | 2014-09-13 | 489 | 6.423818 | https://avatars.githubusercontent.com/u/8749848?v=4 | flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code. | ['complexity-analysis', 'flake8', 'linter', 'linter-flake8', 'pep8', 'static-analysis', 'static-code-analysis', 'style-guide', 'styleguide', 'stylelint'] | ['code-quality', 'complexity-analysis', 'flake8', 'linter', 'linter-flake8', 'pep8', 'static-analysis', 'static-code-analysis', 'style-guide', 'styleguide', 'stylelint'] | 2024-01-08 | [('pycqa/pycodestyle', 0.6883364915847778, 'util', 4), ('pycqa/mccabe', 0.6619266867637634, 'util', 3), ('grantjenks/blue', 0.6205138564109802, 'util', 1), ('hhatto/autopep8', 0.6079347729682922, 'util', 1), ('psf/black', 0.6002591848373413, 'util', 1), ('astral-sh/ruff', 0.5777422785758972, 'util', 7), ('rubik/radon', 0.5651803612709045, 'util', 1), ('google/pytype', 0.5498300194740295, 'typing', 4), ('google/yapf', 0.5436343550682068, 'util', 1), ('pycqa/pylint-django', 0.5301540493965149, 'util', 1), ('pytoolz/toolz', 0.5233083367347717, 'util', 0), ('landscapeio/prospector', 0.5223195552825928, 'util', 0), ('facebook/pyre-check', 0.5048815608024597, 'typing', 2), ('nedbat/coveragepy', 0.5013235807418823, 'testing', 0)] | 174 | 6 | null | 0.92 | 37 | 36 | 114 | 0 | 0 | 9 | 9 | 37 | 48 | 90 | 1.3 | 49 |
1,297 | nlp | https://github.com/promptslab/promptify | ['prompt-engineering'] | null | [] | [] | null | null | null | promptslab/promptify | Promptify | 2,835 | 205 | 46 | Jupyter Notebook | https://discord.gg/m88xfYMbK6 | Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research | promptslab | 2024-01-14 | 2022-12-12 | 59 | 47.934783 | https://avatars.githubusercontent.com/u/120981762?v=4 | Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research | ['chatgpt', 'chatgpt-api', 'chatgpt-python', 'gpt-3', 'gpt-3-prompts', 'gpt-4', 'gpt-4-api', 'gpt3-library', 'large-language-models', 'machine-learning', 'nlp', 'openai', 'prompt-engineering', 'prompt-toolkit', 'prompt-tuning', 'prompt-versioning', 'prompting', 'prompts', 'promptversioning', 'transformers'] | ['chatgpt', 'chatgpt-api', 'chatgpt-python', 'gpt-3', 'gpt-3-prompts', 'gpt-4', 'gpt-4-api', 'gpt3-library', 'large-language-models', 'machine-learning', 'nlp', 'openai', 'prompt-engineering', 'prompt-toolkit', 'prompt-tuning', 'prompt-versioning', 'prompting', 'prompts', 'promptversioning', 'transformers'] | 2023-08-03 | [('promptslab/awesome-prompt-engineering', 0.7121634483337402, 'study', 8), ('guidance-ai/guidance', 0.6470388770103455, 'llm', 2), ('xtekky/gpt4free', 0.6027716398239136, 'llm', 5), ('microsoft/autogen', 0.5999529957771301, 'llm', 2), ('agenta-ai/agenta', 0.5992403030395508, 'llm', 3), ('neulab/prompt2model', 0.5863036513328552, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5769301056861877, 'llm', 1), ('killianlucas/open-interpreter', 0.5764856338500977, 'llm', 2), ('bigscience-workshop/promptsource', 0.5728100538253784, 'nlp', 2), ('hazyresearch/ama_prompting', 0.5550077557563782, 'llm', 1), ('run-llama/rags', 0.5508096814155579, 'llm', 2), ('microsoft/promptbase', 0.5429252982139587, 'llm', 1), ('microsoft/chatgpt-robot-manipulation-prompts', 0.541034460067749, 'llm', 0), ('hazyresearch/manifest', 0.5325891971588135, 'llm', 1), ('microsoft/promptflow', 0.531336784362793, 'llm', 2), ('lianjiatech/belle', 0.5284560322761536, 'llm', 0), ('tmbo/questionary', 0.5267842411994934, 'term', 0), ('microsoft/pycodegpt', 0.5242052674293518, 'llm', 0), ('stanfordnlp/dspy', 0.5224989056587219, 'llm', 1), ('farizrahman4u/loopgpt', 0.5138046741485596, 'llm', 1), ('minimaxir/gpt-2-simple', 0.5102137327194214, 'llm', 1), ('eth-sri/lmql', 0.5044764876365662, 'llm', 2)] | 12 | 4 | null | 8.29 | 8 | 1 | 13 | 5 | 0 | 1 | 1 | 8 | 4 | 90 | 0.5 | 49 |
245 | util | https://github.com/pyston/pyston | [] | null | [] | [] | null | null | null | pyston/pyston | pyston | 2,476 | 90 | 38 | Python | https://www.pyston.org/ | A faster and highly-compatible implementation of the Python programming language. | pyston | 2024-01-14 | 2021-03-01 | 152 | 16.274178 | https://avatars.githubusercontent.com/u/9670621?v=4 | A faster and highly-compatible implementation of the Python programming language. | [] | [] | 2023-02-28 | [('pypy/pypy', 0.7871115207672119, 'util', 0), ('python/cpython', 0.7426720261573792, 'util', 0), ('pytoolz/toolz', 0.719200074672699, 'util', 0), ('cython/cython', 0.6975716352462769, 'util', 0), ('exaloop/codon', 0.6812407970428467, 'perf', 0), ('fastai/fastcore', 0.6732315421104431, 'util', 0), ('sympy/sympy', 0.6676157712936401, 'math', 0), ('eleutherai/pyfra', 0.650770366191864, 'ml', 0), ('micropython/micropython', 0.6496843695640564, 'util', 0), ('ipython/ipyparallel', 0.6226590275764465, 'perf', 0), ('hoffstadt/dearpygui', 0.6223757863044739, 'gui', 0), ('lcompilers/lpython', 0.6024731397628784, 'util', 0), ('joblib/joblib', 0.6001591086387634, 'util', 0), ('pytables/pytables', 0.5999751091003418, 'data', 0), ('pympler/pympler', 0.5993977189064026, 'perf', 0), ('faster-cpython/tools', 0.5962938070297241, 'perf', 0), ('primal100/pybitcointools', 0.5950328707695007, 'crypto', 0), ('ethereum/py-evm', 0.5943373441696167, 'crypto', 0), ('oracle/graalpython', 0.591853141784668, 'util', 0), ('adafruit/circuitpython', 0.5873665809631348, 'util', 0), ('google/latexify_py', 0.5858403444290161, 'util', 0), ('klen/py-frameworks-bench', 0.5815452933311462, 'perf', 0), ('paramiko/paramiko', 0.5798661708831787, 'util', 0), ('numba/llvmlite', 0.5717125535011292, 'util', 0), ('instagram/libcst', 0.5713760852813721, 'util', 0), ('google/jax', 0.5706031918525696, 'ml', 0), ('beeware/toga', 0.5686622262001038, 'gui', 0), ('1200wd/bitcoinlib', 0.5686086416244507, 'crypto', 0), ('ethereum/web3.py', 0.5668970942497253, 'crypto', 0), ('numpy/numpy', 0.5655171871185303, 'math', 0), ('brandtbucher/specialist', 0.5647898316383362, 'perf', 0), ('wxwidgets/phoenix', 0.5643559694290161, 'gui', 0), ('py4j/py4j', 0.5638821721076965, 'util', 0), ('faster-cpython/ideas', 0.5621926188468933, 'perf', 0), ('fredrik-johansson/mpmath', 0.5607538819313049, 'math', 0), ('agronholm/apscheduler', 0.5583381652832031, 'util', 0), ('connorferster/handcalcs', 0.5578861832618713, 'jupyter', 0), ('artemyk/dynpy', 0.5571711659431458, 'sim', 0), ('libtcod/python-tcod', 0.5553798079490662, 'gamedev', 0), ('pyglet/pyglet', 0.5540895462036133, 'gamedev', 0), ('dgilland/cacheout', 0.5537205338478088, 'perf', 0), ('norvig/pytudes', 0.5527113080024719, 'util', 0), ('pytransitions/transitions', 0.5525701642036438, 'util', 0), ('alexmojaki/snoop', 0.5509077310562134, 'debug', 0), ('erotemic/ubelt', 0.5508465766906738, 'util', 0), ('has2k1/plotnine', 0.5506923198699951, 'viz', 0), ('legrandin/pycryptodome', 0.5480275750160217, 'util', 0), ('marshmallow-code/marshmallow', 0.5445635914802551, 'util', 0), ('rustpython/rustpython', 0.543964684009552, 'util', 0), ('klen/muffin', 0.5434106588363647, 'web', 0), ('dosisod/refurb', 0.5432273149490356, 'util', 0), ('python-trio/trio', 0.5414426922798157, 'perf', 0), ('spotify/annoy', 0.5413413643836975, 'ml', 0), ('opengeos/leafmap', 0.5413333773612976, 'gis', 0), ('secdev/scapy', 0.5411231517791748, 'util', 0), ('facebook/pyre-check', 0.5409777760505676, 'typing', 0), ('urwid/urwid', 0.5381410121917725, 'term', 0), ('pexpect/pexpect', 0.5372031331062317, 'util', 0), ('crunch-io/lazycsv', 0.5371149182319641, 'perf', 0), ('google/pyglove', 0.5362030863761902, 'util', 0), ('python-rope/rope', 0.5357602834701538, 'util', 0), ('xrudelis/pytrait', 0.5349142551422119, 'util', 0), ('pandas-dev/pandas', 0.5344043970108032, 'pandas', 0), ('pythonspeed/filprofiler', 0.5313695073127747, 'profiling', 0), ('pyomo/pyomo', 0.5302437543869019, 'math', 0), ('pdm-project/pdm', 0.530032217502594, 'util', 0), ('webpy/webpy', 0.5299744606018066, 'web', 0), ('pypa/installer', 0.5297871828079224, 'util', 0), ('pyca/cryptography', 0.5291821360588074, 'util', 0), ('amaargiru/pyroad', 0.5288054347038269, 'study', 0), ('irmen/pyminiaudio', 0.5274853110313416, 'util', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5267325639724731, 'template', 0), ('stanfordnlp/dspy', 0.5251495838165283, 'llm', 0), ('altair-viz/altair', 0.5246127843856812, 'viz', 0), ('zerointensity/pointers.py', 0.523978590965271, 'perf', 0), ('mynameisfiber/high_performance_python_2e', 0.5236924290657043, 'study', 0), ('masoniteframework/masonite', 0.523208498954773, 'web', 0), ('ibis-project/ibis', 0.5222876667976379, 'data', 0), ('pyparsing/pyparsing', 0.522191047668457, 'util', 0), ('willmcgugan/textual', 0.5220023393630981, 'term', 0), ('imageio/imageio', 0.5213198661804199, 'util', 0), ('google/gin-config', 0.5207974910736084, 'util', 0), ('scipy/scipy', 0.5205775499343872, 'math', 0), ('pybind/pybind11', 0.5205539464950562, 'perf', 0), ('tiangolo/sqlmodel', 0.5203248858451843, 'data', 0), ('nvidia/warp', 0.5197428464889526, 'sim', 0), ('julienpalard/pipe', 0.5197369456291199, 'util', 0), ('astral-sh/ruff', 0.5196829438209534, 'util', 0), ('cherrypy/cherrypy', 0.5174131989479065, 'web', 0), ('pemistahl/lingua-py', 0.5171454548835754, 'nlp', 0), ('pygments/pygments', 0.5150144696235657, 'util', 0), ('nedbat/coveragepy', 0.5141503810882568, 'testing', 0), ('rasbt/watermark', 0.5138229131698608, 'util', 0), ('hhatto/autopep8', 0.5110304355621338, 'util', 0), ('evhub/coconut', 0.5107958316802979, 'util', 0), ('requests/toolbelt', 0.5103037357330322, 'util', 0), ('zarr-developers/zarr-python', 0.5102282762527466, 'data', 0), ('plasma-umass/scalene', 0.5100093483924866, 'profiling', 0), ('amzn/ion-python', 0.5089343786239624, 'data', 0), ('pyeve/cerberus', 0.5083901882171631, 'data', 0), ('kubeflow/fairing', 0.508223295211792, 'ml-ops', 0), ('pyscript/pyscript-cli', 0.5076680183410645, 'web', 0), ('gbeced/pyalgotrade', 0.507022500038147, 'finance', 0), ('timofurrer/awesome-asyncio', 0.5064808130264282, 'study', 0), ('nmslib/hnswlib', 0.5059448480606079, 'ml', 0), ('pylons/pyramid', 0.5057663321495056, 'web', 0), ('collerek/ormar', 0.5057287812232971, 'data', 0), ('scrapy/scrapy', 0.5049728751182556, 'data', 0), ('pypa/hatch', 0.5036124587059021, 'util', 0), ('ethtx/ethtx', 0.5028896331787109, 'crypto', 0), ('holoviz/holoviz', 0.5020092725753784, 'viz', 0), ('encode/httpx', 0.5002996325492859, 'web', 0), ('google/yapf', 0.5001851320266724, 'util', 0)] | 1,037 | 6 | null | 0.04 | 1 | 0 | 35 | 11 | 0 | 164 | 164 | 1 | 2 | 90 | 2 | 49 |
1,739 | ml-rl | https://github.com/eureka-research/eureka | ['evolutionary-optimization'] | null | [] | [] | null | null | null | eureka-research/eureka | Eureka | 2,376 | 197 | 22 | Jupyter Notebook | https://eureka-research.github.io/ | Official Repository for "Eureka: Human-Level Reward Design via Coding Large Language Models" | eureka-research | 2024-01-14 | 2023-09-25 | 18 | 130.96063 | https://avatars.githubusercontent.com/u/145279285?v=4 | Official Repository for "Eureka: Human-Level Reward Design via Coding Large Language Models" | [] | ['evolutionary-optimization'] | 2023-11-17 | [('yueyu1030/attrprompt', 0.522046685218811, 'llm', 0), ('lupantech/chameleon-llm', 0.5140032768249512, 'llm', 0)] | 2 | 0 | null | 0.12 | 36 | 8 | 4 | 2 | 0 | 0 | 0 | 36 | 43 | 90 | 1.2 | 49 |
1,356 | term | https://github.com/textualize/trogon | ['click', 'cli', 'terminal', 'textual'] | null | [] | [] | null | null | null | textualize/trogon | trogon | 2,229 | 44 | 20 | Python | null | Easily turn your Click CLI into a powerful terminal application | textualize | 2024-01-12 | 2023-04-18 | 41 | 54.365854 | https://avatars.githubusercontent.com/u/93378883?v=4 | Easily turn your Click CLI into a powerful terminal application | [] | ['cli', 'click', 'terminal', 'textual'] | 2023-08-21 | [('tiangolo/typer', 0.5765345692634583, 'term', 3), ('jquast/blessed', 0.5472238063812256, 'term', 2), ('pallets/click', 0.5391209125518799, 'term', 3), ('click-contrib/click-completion', 0.5366969704627991, 'term', 1), ('pexpect/pexpect', 0.5282593965530396, 'util', 0), ('python-poetry/cleo', 0.5179005265235901, 'term', 1), ('pyscript/pyscript-cli', 0.516979992389679, 'web', 0)] | 6 | 3 | null | 3.12 | 14 | 2 | 9 | 5 | 3 | 4 | 3 | 14 | 9 | 90 | 0.6 | 49 |
527 | ml-dl | https://github.com/neuralmagic/sparseml | [] | null | [] | [] | null | null | null | neuralmagic/sparseml | sparseml | 1,893 | 135 | 46 | Python | null | Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models | neuralmagic | 2024-01-13 | 2020-12-11 | 163 | 11.572926 | https://avatars.githubusercontent.com/u/68670575?v=4 | Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models | ['automl', 'computer-vision-algorithms', 'deep-learning-algorithms', 'deep-learning-library', 'deep-learning-models', 'image-classification', 'keras', 'nlp', 'object-detection', 'onnx', 'pruning', 'pruning-algorithms', 'pytorch', 'smaller-models', 'sparsification', 'sparsification-recipes', 'sparsity', 'tensorflow', 'transfer-learning'] | ['automl', 'computer-vision-algorithms', 'deep-learning-algorithms', 'deep-learning-library', 'deep-learning-models', 'image-classification', 'keras', 'nlp', 'object-detection', 'onnx', 'pruning', 'pruning-algorithms', 'pytorch', 'smaller-models', 'sparsification', 'sparsification-recipes', 'sparsity', 'tensorflow', 'transfer-learning'] | 2024-01-12 | [('neuralmagic/deepsparse', 0.7135436534881592, 'nlp', 5), ('lutzroeder/netron', 0.6401857137680054, 'ml', 4), ('mosaicml/composer', 0.6215312480926514, 'ml-dl', 1), ('pytorch/ignite', 0.6128144860267639, 'ml-dl', 1), ('karpathy/micrograd', 0.597611129283905, 'study', 0), ('skorch-dev/skorch', 0.5961683988571167, 'ml-dl', 1), ('explosion/thinc', 0.5918089747428894, 'ml-dl', 3), ('huggingface/transformers', 0.5908825993537903, 'nlp', 3), ('rafiqhasan/auto-tensorflow', 0.5831721425056458, 'ml-dl', 2), ('keras-team/autokeras', 0.5828292369842529, 'ml-dl', 3), ('rasbt/machine-learning-book', 0.5774632692337036, 'study', 1), ('intel/intel-extension-for-pytorch', 0.5700653195381165, 'perf', 1), ('nyandwi/modernconvnets', 0.567727267742157, 'ml-dl', 4), ('microsoft/nni', 0.565841555595398, 'ml', 3), ('alpa-projects/alpa', 0.560257613658905, 'ml-dl', 0), ('ashleve/lightning-hydra-template', 0.5597487688064575, 'util', 1), ('uber/petastorm', 0.5541288256645203, 'data', 2), ('ludwig-ai/ludwig', 0.5519493222236633, 'ml-ops', 1), ('nvidia/deeplearningexamples', 0.5509819984436035, 'ml-dl', 3), ('tensorflow/addons', 0.5490881204605103, 'ml', 1), ('ggerganov/ggml', 0.5481603741645813, 'ml', 0), ('huggingface/datasets', 0.5456334948539734, 'nlp', 3), ('awslabs/autogluon', 0.5449380278587341, 'ml', 5), ('albumentations-team/albumentations', 0.5420981645584106, 'ml-dl', 2), ('aleju/imgaug', 0.5411894917488098, 'ml', 0), ('arogozhnikov/einops', 0.5392698645591736, 'ml-dl', 3), ('cvxgrp/pymde', 0.5381171107292175, 'ml', 1), ('tensorflow/tensorflow', 0.5374124050140381, 'ml-dl', 1), ('oml-team/open-metric-learning', 0.5373204350471497, 'ml', 1), ('deepfakes/faceswap', 0.5372126698493958, 'ml-dl', 0), ('horovod/horovod', 0.5365691781044006, 'ml-ops', 3), ('microsoft/deepspeed', 0.5357862114906311, 'ml-dl', 1), ('ddbourgin/numpy-ml', 0.5339189171791077, 'ml', 0), ('huggingface/exporters', 0.5338050127029419, 'ml', 2), ('explosion/spacy-streamlit', 0.5314697623252869, 'nlp', 1), ('pyg-team/pytorch_geometric', 0.5272446274757385, 'ml-dl', 1), ('onnx/onnx', 0.5271431803703308, 'ml', 4), ('explosion/spacy-transformers', 0.525894284248352, 'llm', 3), ('microsoft/flaml', 0.525197446346283, 'ml', 1), ('rwightman/pytorch-image-models', 0.5248225331306458, 'ml-dl', 1), ('fepegar/torchio', 0.5229683518409729, 'ml-dl', 1), ('christoschristofidis/awesome-deep-learning', 0.5221423506736755, 'study', 0), ('aiqc/aiqc', 0.5212861895561218, 'ml-ops', 0), ('roboflow/notebooks', 0.5195624828338623, 'study', 3), ('tensorlayer/tensorlayer', 0.5187444090843201, 'ml-rl', 2), ('deci-ai/super-gradients', 0.5168516039848328, 'ml-dl', 4), ('tensorflow/tensor2tensor', 0.5167617201805115, 'ml', 0), ('tensorly/tensorly', 0.5156332850456238, 'ml-dl', 2), ('mrdbourke/pytorch-deep-learning', 0.5144563317298889, 'study', 1), ('lucidrains/imagen-pytorch', 0.512857973575592, 'ml-dl', 0), ('qdrant/quaterion', 0.5124584436416626, 'ml', 1), ('huggingface/optimum', 0.5119937658309937, 'ml', 2), ('roboflow/supervision', 0.5113904476165771, 'ml', 3), ('explosion/spacy-models', 0.5111375451087952, 'nlp', 1), ('aistream-peelout/flow-forecast', 0.5107213854789734, 'time-series', 2), ('towhee-io/towhee', 0.5096480250358582, 'ml-ops', 0), ('mdbloice/augmentor', 0.5088579654693604, 'ml', 0), ('keras-team/keras', 0.5088281631469727, 'ml-dl', 2), ('nccr-itmo/fedot', 0.508357048034668, 'ml-ops', 1), ('huggingface/neuralcoref', 0.5070856213569641, 'nlp', 2), ('iperov/deepfacelab', 0.5047088861465454, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5017151236534119, 'ml', 3)] | 47 | 2 | null | 6.75 | 220 | 198 | 38 | 0 | 11 | 12 | 11 | 219 | 79 | 90 | 0.4 | 49 |
433 | data | https://github.com/sdv-dev/sdv | [] | null | [] | [] | null | null | null | sdv-dev/sdv | SDV | 1,775 | 255 | 42 | Python | https://docs.sdv.dev/sdv | Synthetic data generation for tabular data | sdv-dev | 2024-01-13 | 2018-05-11 | 298 | 5.944976 | https://avatars.githubusercontent.com/u/59050321?v=4 | Synthetic data generation for tabular data | ['data-generation', 'deep-learning', 'gan', 'gans', 'generative-adversarial-network', 'generative-ai', 'generative-model', 'generativeai', 'machine-learning', 'multi-table', 'relational-datasets', 'sdv', 'synthetic-data', 'synthetic-data-generation', 'time-series'] | ['data-generation', 'deep-learning', 'gan', 'gans', 'generative-adversarial-network', 'generative-ai', 'generative-model', 'generativeai', 'machine-learning', 'multi-table', 'relational-datasets', 'sdv', 'synthetic-data', 'synthetic-data-generation', 'time-series'] | 2024-01-11 | [('ydataai/ydata-synthetic', 0.9112098217010498, 'data', 7), ('nicolas-hbt/pygraft', 0.6006342172622681, 'ml', 2), ('awslabs/autogluon', 0.562446117401123, 'ml', 3), ('makcedward/nlpaug', 0.517977237701416, 'nlp', 1)] | 46 | 5 | null | 5.25 | 147 | 116 | 69 | 0 | 13 | 10 | 13 | 146 | 192 | 90 | 1.3 | 49 |
1,529 | llm | https://github.com/spcl/graph-of-thoughts | [] | null | [] | [] | 1 | null | null | spcl/graph-of-thoughts | graph-of-thoughts | 1,675 | 102 | 19 | Python | https://arxiv.org/pdf/2308.09687.pdf | Official Implementation of "Graph of Thoughts: Solving Elaborate Problems with Large Language Models" | spcl | 2024-01-14 | 2023-08-18 | 23 | 71.060606 | https://avatars.githubusercontent.com/u/31108244?v=4 | Official Implementation of "Graph of Thoughts: Solving Elaborate Problems with Large Language Models" | ['graph-of-thoughts', 'graph-structures', 'graphs', 'large-language-models', 'llm', 'prompt-engineering', 'prompting'] | ['graph-of-thoughts', 'graph-structures', 'graphs', 'large-language-models', 'llm', 'prompt-engineering', 'prompting'] | 2023-12-02 | [('kyegomez/tree-of-thoughts', 0.6711254715919495, 'llm', 1), ('dylanhogg/llmgraph', 0.6043452024459839, 'ml', 1), ('hannibal046/awesome-llm', 0.5740511417388916, 'study', 0), ('keirp/automatic_prompt_engineer', 0.5692050457000732, 'llm', 1), ('guidance-ai/guidance', 0.5539388060569763, 'llm', 1), ('lianjiatech/belle', 0.5425434708595276, 'llm', 0), ('lupantech/chameleon-llm', 0.5331078171730042, 'llm', 1), ('langchain-ai/langgraph', 0.5278857350349426, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5230525732040405, 'study', 1), ('microsoft/autogen', 0.5202249884605408, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5085147023200989, 'llm', 0)] | 4 | 1 | null | 0.37 | 16 | 14 | 5 | 1 | 2 | 5 | 2 | 16 | 12 | 90 | 0.8 | 49 |
1,858 | sim | https://github.com/nvidia/warp | ['simulation', 'gpu'] | null | [] | [] | null | null | null | nvidia/warp | warp | 1,472 | 118 | 43 | Python | https://nvidia.github.io/warp/ | A Python framework for high performance GPU simulation and graphics | nvidia | 2024-01-13 | 2022-03-18 | 97 | 15.086384 | https://avatars.githubusercontent.com/u/1728152?v=4 | A Python framework for high performance GPU simulation and graphics | [] | ['gpu', 'simulation'] | 2024-01-11 | [('pytorch/pytorch', 0.6113191843032837, 'ml-dl', 1), ('exaloop/codon', 0.603724479675293, 'perf', 0), ('rapidsai/jupyterlab-nvdashboard', 0.5572924017906189, 'jupyter', 1), ('google/jax', 0.5563659071922302, 'ml', 0), ('nvidia/tensorrt-llm', 0.5474246740341187, 'viz', 1), ('joblib/joblib', 0.5375118851661682, 'util', 0), ('google/tf-quant-finance', 0.5373433828353882, 'finance', 1), ('klen/py-frameworks-bench', 0.5304505825042725, 'perf', 0), ('google/gin-config', 0.5256108641624451, 'util', 0), ('pokepetter/ursina', 0.5230095982551575, 'gamedev', 0), ('pyston/pyston', 0.5197428464889526, 'util', 0), ('ipython/ipyparallel', 0.5195223093032837, 'perf', 0), ('cupy/cupy', 0.518425464630127, 'math', 1), ('huggingface/accelerate', 0.5148420333862305, 'ml', 0), ('micropython/micropython', 0.512362003326416, 'util', 0), ('numpy/numpy', 0.5096368193626404, 'math', 0), ('fastai/fastcore', 0.5028382539749146, 'util', 0), ('facebookincubator/aitemplate', 0.502746045589447, 'ml-dl', 0), ('eleutherai/gpt-neo', 0.5011614561080933, 'llm', 0)] | 34 | 2 | null | 17.08 | 56 | 25 | 22 | 0 | 5 | 12 | 5 | 56 | 45 | 90 | 0.8 | 49 |
1,069 | llm | https://github.com/microsoft/megatron-deepspeed | [] | null | [] | [] | null | null | null | microsoft/megatron-deepspeed | Megatron-DeepSpeed | 1,353 | 262 | 19 | Python | null | Ongoing research training transformer language models at scale, including: BERT & GPT-2 | microsoft | 2024-01-13 | 2021-06-21 | 136 | 9.93809 | https://avatars.githubusercontent.com/u/6154722?v=4 | Ongoing research training transformer language models at scale, including: BERT & GPT-2 | [] | [] | 2024-01-10 | [('bigscience-workshop/megatron-deepspeed', 1.0000001192092896, 'llm', 0), ('nvidia/megatron-lm', 0.6671424508094788, 'llm', 0), ('lvwerra/trl', 0.6662755608558655, 'llm', 0), ('jonasgeiping/cramming', 0.6582860946655273, 'nlp', 0), ('huggingface/transformers', 0.6457441449165344, 'nlp', 0), ('explosion/spacy-transformers', 0.6363678574562073, 'llm', 0), ('hannibal046/awesome-llm', 0.6277967095375061, 'study', 0), ('extreme-bert/extreme-bert', 0.6164913773536682, 'llm', 0), ('graykode/nlp-tutorial', 0.6075314879417419, 'study', 0), ('karpathy/mingpt', 0.6039530634880066, 'llm', 0), ('lianjiatech/belle', 0.5846147537231445, 'llm', 0), ('huggingface/text-generation-inference', 0.5798518061637878, 'llm', 0), ('nielsrogge/transformers-tutorials', 0.5679675936698914, 'study', 0), ('next-gpt/next-gpt', 0.5671409964561462, 'llm', 0), ('whu-zqh/chatgpt-vs.-bert', 0.5593957901000977, 'llm', 0), ('eleutherai/gpt-neo', 0.5555706024169922, 'llm', 0), ('eleutherai/knowledge-neurons', 0.548306405544281, 'ml-interpretability', 0), ('ai21labs/lm-evaluation', 0.5460460186004639, 'llm', 0), ('microsoft/lora', 0.5398515462875366, 'llm', 0), ('minimaxir/gpt-2-simple', 0.5385422110557556, 'llm', 0), ('deepset-ai/farm', 0.5374853014945984, 'nlp', 0), ('jalammar/ecco', 0.5253652930259705, 'ml-interpretability', 0), ('promptslab/awesome-prompt-engineering', 0.5253517031669617, 'study', 0), ('bigscience-workshop/biomedical', 0.5243796706199646, 'data', 0), ('xtekky/gpt4free', 0.5237749814987183, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5230602622032166, 'llm', 0), ('ist-daslab/gptq', 0.5179560780525208, 'llm', 0), ('jina-ai/finetuner', 0.5171146988868713, 'ml', 0), ('bytedance/lightseq', 0.515959620475769, 'nlp', 0), ('lm-sys/fastchat', 0.51581871509552, 'llm', 0), ('alignmentresearch/tuned-lens', 0.515650749206543, 'ml-interpretability', 0), ('freedomintelligence/llmzoo', 0.5145018100738525, 'llm', 0), ('cdpierse/transformers-interpret', 0.5142018795013428, 'ml-interpretability', 0), ('bobazooba/xllm', 0.5139332413673401, 'llm', 0), ('lucidrains/toolformer-pytorch', 0.5127219557762146, 'llm', 0), ('llmware-ai/llmware', 0.5115044116973877, 'llm', 0), ('microsoft/autogen', 0.5109838843345642, 'llm', 0), ('salesforce/blip', 0.5101778507232666, 'diffusion', 0), ('thilinarajapakse/simpletransformers', 0.5100935697555542, 'nlp', 0), ('openai/finetune-transformer-lm', 0.5082074999809265, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5077611804008484, 'llm', 0), ('openai/gpt-2', 0.5042035579681396, 'llm', 0), ('alibaba/easynlp', 0.5024697184562683, 'nlp', 0), ('muennighoff/sgpt', 0.5024375915527344, 'llm', 0)] | 122 | 3 | null | 5.9 | 86 | 45 | 31 | 0 | 0 | 4 | 4 | 86 | 115 | 90 | 1.3 | 49 |
973 | ml | https://github.com/googlecloudplatform/vertex-ai-samples | [] | null | [] | [] | null | null | null | googlecloudplatform/vertex-ai-samples | vertex-ai-samples | 1,150 | 649 | 40 | Jupyter Notebook | https://cloud.google.com/vertex-ai | Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud | googlecloudplatform | 2024-01-11 | 2021-05-27 | 139 | 8.231084 | https://avatars.githubusercontent.com/u/2810941?v=4 | Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud | ['ai', 'data-science', 'gcp', 'google-cloud-platform', 'ml', 'mlops', 'notebook', 'samples', 'vertex-ai'] | ['ai', 'data-science', 'gcp', 'google-cloud-platform', 'ml', 'mlops', 'notebook', 'samples', 'vertex-ai'] | 2024-01-12 | [('google-research/google-research', 0.6550525426864624, 'ml', 1), ('mlflow/mlflow', 0.6433287262916565, 'ml-ops', 2), ('bentoml/bentoml', 0.6409500241279602, 'ml-ops', 2), ('pytorchlightning/pytorch-lightning', 0.6309098601341248, 'ml-dl', 2), ('hpcaitech/colossalai', 0.6214262843132019, 'llm', 1), ('netflix/metaflow', 0.6179714798927307, 'ml-ops', 5), ('polyaxon/polyaxon', 0.6127983331680298, 'ml-ops', 4), ('skypilot-org/skypilot', 0.6106062531471252, 'llm', 1), ('google-research/language', 0.6063291430473328, 'nlp', 0), ('jina-ai/jina', 0.6033614873886108, 'ml', 1), ('feast-dev/feast', 0.595111072063446, 'ml-ops', 3), ('activeloopai/deeplake', 0.5941661596298218, 'ml-ops', 4), ('mindsdb/mindsdb', 0.5918219685554504, 'data', 2), ('determined-ai/determined', 0.5860880613327026, 'ml-ops', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5835177302360535, 'study', 1), ('qdrant/qdrant', 0.5779662132263184, 'data', 1), ('sweepai/sweep', 0.5760075449943542, 'llm', 1), ('oegedijk/explainerdashboard', 0.5758024454116821, 'ml-interpretability', 0), ('tensorflow/tensorflow', 0.5740510821342468, 'ml-dl', 1), ('ml-tooling/opyrator', 0.5720667243003845, 'viz', 0), ('cheshire-cat-ai/core', 0.5718985199928284, 'llm', 1), ('firmai/industry-machine-learning', 0.5715494751930237, 'study', 1), ('marqo-ai/marqo', 0.5693247318267822, 'ml', 0), ('deepmind/dm_control', 0.569223940372467, 'ml-rl', 0), ('microsoft/nni', 0.5683177709579468, 'ml', 2), ('unity-technologies/ml-agents', 0.5682862997055054, 'ml-rl', 0), ('mlc-ai/mlc-llm', 0.5680209994316101, 'llm', 0), ('huggingface/datasets', 0.5620868802070618, 'nlp', 0), ('merantix-momentum/squirrel-core', 0.5609938502311707, 'ml', 3), ('onnx/onnx', 0.558918833732605, 'ml', 1), ('chandlerbang/awesome-self-supervised-gnn', 0.5588842034339905, 'study', 0), ('googlecloudplatform/python-docs-samples', 0.5574522018432617, 'util', 1), ('alpa-projects/alpa', 0.5572735667228699, 'ml-dl', 0), ('polyaxon/datatile', 0.5558412075042725, 'pandas', 2), ('gradio-app/gradio', 0.5544818639755249, 'viz', 1), ('patchy631/machine-learning', 0.5536148548126221, 'ml', 0), ('ray-project/ray', 0.549000084400177, 'ml-ops', 1), ('microsoft/onnxruntime', 0.5478015542030334, 'ml', 0), ('prefecthq/marvin', 0.5464761853218079, 'nlp', 1), ('danielegrattarola/spektral', 0.545731782913208, 'ml-dl', 0), ('wandb/client', 0.5439368486404419, 'ml', 2), ('superduperdb/superduperdb', 0.5415635704994202, 'data', 3), ('stellargraph/stellargraph', 0.5413510799407959, 'graph', 1), ('google/dopamine', 0.5409380793571472, 'ml-rl', 2), ('iterative/dvc', 0.5397675633430481, 'ml-ops', 2), ('aimhubio/aim', 0.5386273264884949, 'ml-ops', 4), ('ddbourgin/numpy-ml', 0.5378352403640747, 'ml', 0), ('antonosika/gpt-engineer', 0.5366024374961853, 'llm', 1), ('keras-rl/keras-rl', 0.5353646278381348, 'ml-rl', 0), ('xplainable/xplainable', 0.53435218334198, 'ml-interpretability', 1), ('ludwig-ai/ludwig', 0.5321346521377563, 'ml-ops', 2), ('titanml/takeoff', 0.531377375125885, 'llm', 0), ('deepchecks/deepchecks', 0.5285258889198303, 'data', 3), ('explosion/thinc', 0.5279651284217834, 'ml-dl', 1), ('google/mediapipe', 0.5271955132484436, 'ml', 0), ('transformeroptimus/superagi', 0.5266525745391846, 'llm', 1), ('mosaicml/composer', 0.525791585445404, 'ml-dl', 0), ('avaiga/taipy', 0.5246182680130005, 'data', 1), ('microsoft/lmops', 0.5236150026321411, 'llm', 0), ('giskard-ai/giskard', 0.5234065651893616, 'data', 1), ('googlecloudplatform/practical-ml-vision-book', 0.5228831768035889, 'study', 0), ('rasbt/machine-learning-book', 0.5225531458854675, 'study', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5220873951911926, 'study', 0), ('tensorlayer/tensorlayer', 0.5206416845321655, 'ml-rl', 0), ('kubeflow-kale/kale', 0.5194539427757263, 'ml-ops', 0), ('tensorflow/tensor2tensor', 0.5193653702735901, 'ml', 0), ('uber/fiber', 0.5184296369552612, 'data', 0), ('google/tf-quant-finance', 0.5174439549446106, 'finance', 0), ('kubeflow/pipelines', 0.5168536901473999, 'ml-ops', 2), ('pathwaycom/llm-app', 0.5163154006004333, 'llm', 0), ('adap/flower', 0.5161627531051636, 'ml-ops', 1), ('whylabs/whylogs', 0.5152586102485657, 'util', 2), ('operand/agency', 0.5143194198608398, 'llm', 1), ('pythagora-io/gpt-pilot', 0.513899564743042, 'llm', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5130940079689026, 'study', 0), ('dialogflow/dialogflow-python-client-v2', 0.5129502415657043, 'nlp', 0), ('horovod/horovod', 0.5119903683662415, 'ml-ops', 0), ('nebuly-ai/nebullvm', 0.5115224719047546, 'perf', 1), ('arize-ai/phoenix', 0.5083956122398376, 'ml-interpretability', 1), ('aws/sagemaker-python-sdk', 0.5073953866958618, 'ml', 0), ('dagworks-inc/hamilton', 0.5069316625595093, 'ml-ops', 2), ('keras-team/autokeras', 0.5066843032836914, 'ml-dl', 0), ('kubeflow/fairing', 0.5057130455970764, 'ml-ops', 0), ('online-ml/river', 0.505128800868988, 'ml', 1), ('lithops-cloud/lithops', 0.5040023922920227, 'ml-ops', 0), ('bigscience-workshop/petals', 0.5039993524551392, 'data', 0), ('rasahq/rasa', 0.5028780102729797, 'llm', 0), ('rafiqhasan/auto-tensorflow', 0.5013206601142883, 'ml-dl', 0), ('lutzroeder/netron', 0.5002110004425049, 'ml', 2)] | 141 | 5 | null | 19.94 | 297 | 270 | 32 | 0 | 0 | 0 | 0 | 297 | 294 | 90 | 1 | 49 |
1,508 | llm | https://github.com/ajndkr/lanarky | [] | null | [] | [] | null | null | null | ajndkr/lanarky | lanarky | 900 | 65 | 14 | Python | https://lanarky.ajndkr.com/ | The web framework for building LLM microservices | ajndkr | 2024-01-12 | 2023-04-07 | 42 | 21.14094 | null | The web framework for building LLM microservices | ['fastapi', 'llmops', 'web'] | ['fastapi', 'llmops', 'web'] | 2024-01-13 | [('janetech-inc/fast-api-admin-template', 0.6117317080497742, 'template', 0), ('berriai/litellm', 0.587331235408783, 'llm', 1), ('young-geng/easylm', 0.5861847996711731, 'llm', 0), ('pathwaycom/llm-app', 0.5696076154708862, 'llm', 1), ('tiangolo/fastapi', 0.5630785822868347, 'web', 2), ('shishirpatil/gorilla', 0.5609325766563416, 'llm', 0), ('run-llama/llama-hub', 0.5594974160194397, 'data', 0), ('alpha-vllm/llama2-accessory', 0.552966833114624, 'llm', 0), ('jerryjliu/llama_index', 0.5451417565345764, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5414519309997559, 'llm', 0), ('ml-tooling/opyrator', 0.5310590267181396, 'viz', 1), ('deepset-ai/haystack', 0.5300788283348083, 'llm', 0), ('run-llama/llama-lab', 0.5272144079208374, 'llm', 0), ('unionai-oss/unionml', 0.5264783501625061, 'ml-ops', 0), ('falconry/falcon', 0.5218005180358887, 'web', 1), ('eugeneyan/open-llms', 0.5215867757797241, 'study', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5205399394035339, 'template', 1), ('pallets/quart', 0.5203720331192017, 'web', 0), ('hwchase17/langchain', 0.5159770846366882, 'llm', 0), ('bentoml/openllm', 0.5158595442771912, 'ml-ops', 1), ('pallets/flask', 0.510615348815918, 'web', 0), ('microsoft/semantic-kernel', 0.5084496140480042, 'llm', 0), ('agenta-ai/agenta', 0.5057575702667236, 'llm', 1)] | 13 | 2 | null | 3.06 | 53 | 47 | 9 | 0 | 48 | 65 | 48 | 53 | 62 | 90 | 1.2 | 49 |
1,318 | ml-dl | https://github.com/nvidia/deeplearningexamples | [] | null | [] | [] | null | null | null | nvidia/deeplearningexamples | DeepLearningExamples | 12,049 | 2,999 | 295 | Jupyter Notebook | null | State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure. | nvidia | 2024-01-14 | 2018-05-02 | 299 | 40.182468 | https://avatars.githubusercontent.com/u/1728152?v=4 | State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure. | ['computer-vision', 'deep-learning', 'drug-discovery', 'forecasting', 'large-language-models', 'mxnet', 'nlp', 'paddlepaddle', 'pytorch', 'recommender-systems', 'speech-recognition', 'speech-synthesis', 'tensorflow', 'tensorflow2', 'translation'] | ['computer-vision', 'deep-learning', 'drug-discovery', 'forecasting', 'large-language-models', 'mxnet', 'nlp', 'paddlepaddle', 'pytorch', 'recommender-systems', 'speech-recognition', 'speech-synthesis', 'tensorflow', 'tensorflow2', 'translation'] | 2023-12-08 | [('huggingface/transformers', 0.6816701889038086, 'nlp', 5), ('huggingface/datasets', 0.6591049432754517, 'nlp', 5), ('explosion/thinc', 0.6549383401870728, 'ml-dl', 5), ('ddbourgin/numpy-ml', 0.6215345859527588, 'ml', 0), ('keras-team/keras-nlp', 0.6211730241775513, 'nlp', 3), ('tensorflow/tensor2tensor', 0.6179958581924438, 'ml', 1), ('aiqc/aiqc', 0.6063768267631531, 'ml-ops', 0), ('microsoft/deepspeed', 0.6031937003135681, 'ml-dl', 2), ('bentoml/bentoml', 0.6029508113861084, 'ml-ops', 1), ('tensorflow/tensorflow', 0.5997220873832703, 'ml-dl', 2), ('keras-team/keras', 0.5996661186218262, 'ml-dl', 3), ('apache/incubator-mxnet', 0.5984542965888977, 'ml-dl', 1), ('llmware-ai/llmware', 0.5966246128082275, 'llm', 3), ('tensorlayer/tensorlayer', 0.5958699584007263, 'ml-rl', 2), ('pytorch/ignite', 0.5919349193572998, 'ml-dl', 2), ('d2l-ai/d2l-en', 0.5897380709648132, 'study', 5), ('rasbt/machine-learning-book', 0.5868580937385559, 'study', 2), ('lutzroeder/netron', 0.5856183767318726, 'ml', 4), ('optimalscale/lmflow', 0.5852581858634949, 'llm', 2), ('horovod/horovod', 0.5849502086639404, 'ml-ops', 4), ('rafiqhasan/auto-tensorflow', 0.5845487713813782, 'ml-dl', 1), ('karpathy/micrograd', 0.583264946937561, 'study', 0), ('microsoft/onnxruntime', 0.5810919404029846, 'ml', 3), ('deeppavlov/deeppavlov', 0.5808957815170288, 'nlp', 3), ('google/trax', 0.5805593132972717, 'ml-dl', 1), ('extreme-bert/extreme-bert', 0.5793726444244385, 'llm', 3), ('microsoft/nni', 0.5766809582710266, 'ml', 3), ('lucidrains/toolformer-pytorch', 0.576062798500061, 'llm', 1), ('keras-team/autokeras', 0.5739009976387024, 'ml-dl', 2), ('alpa-projects/alpa', 0.5713871717453003, 'ml-dl', 1), ('intel/intel-extension-for-pytorch', 0.5710954070091248, 'perf', 2), ('determined-ai/determined', 0.5695421099662781, 'ml-ops', 3), ('graykode/nlp-tutorial', 0.569402277469635, 'study', 3), ('pytorchlightning/pytorch-lightning', 0.5674681067466736, 'ml-dl', 2), ('allenai/allennlp', 0.5669660568237305, 'nlp', 3), ('mosaicml/composer', 0.5654811859130859, 'ml-dl', 2), ('ageron/handson-ml2', 0.5652621388435364, 'ml', 0), ('gradio-app/gradio', 0.5647919774055481, 'viz', 1), ('arogozhnikov/einops', 0.5647768378257751, 'ml-dl', 3), ('onnx/onnx', 0.5635833144187927, 'ml', 4), ('alibaba/easynlp', 0.5610079169273376, 'nlp', 3), ('tlkh/tf-metal-experiments', 0.5584723949432373, 'perf', 2), ('deepmodeling/deepmd-kit', 0.558430016040802, 'sim', 2), ('uber/petastorm', 0.5578290820121765, 'data', 3), ('rwightman/pytorch-image-models', 0.5575792193412781, 'ml-dl', 1), ('ludwig-ai/ludwig', 0.557460367679596, 'ml-ops', 3), ('neuralmagic/deepsparse', 0.554892361164093, 'nlp', 2), ('rasbt/deeplearning-models', 0.554612934589386, 'ml-dl', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5539207458496094, 'study', 1), ('tatsu-lab/stanford_alpaca', 0.5533385872840881, 'llm', 1), ('neuralmagic/sparseml', 0.5509819984436035, 'ml-dl', 3), ('christoschristofidis/awesome-deep-learning', 0.55058354139328, 'study', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5502544045448303, 'study', 0), ('aistream-peelout/flow-forecast', 0.549731969833374, 'time-series', 3), ('explosion/spacy', 0.5494264364242554, 'nlp', 2), ('databrickslabs/dolly', 0.548334538936615, 'llm', 0), ('kubeflow/fairing', 0.548143744468689, 'ml-ops', 0), ('tensorflow/lucid', 0.5472056269645691, 'ml-interpretability', 1), ('nyandwi/modernconvnets', 0.5466137528419495, 'ml-dl', 2), ('ray-project/ray', 0.5435977578163147, 'ml-ops', 3), ('salesforce/deeptime', 0.5429381728172302, 'time-series', 2), ('activeloopai/deeplake', 0.5426178574562073, 'ml-ops', 5), ('polyaxon/polyaxon', 0.5416098833084106, 'ml-ops', 4), ('microsoft/semi-supervised-learning', 0.5403677821159363, 'ml', 3), ('deepset-ai/farm', 0.5379154086112976, 'nlp', 3), ('deepchecks/deepchecks', 0.537693440914154, 'data', 2), ('merantix-momentum/squirrel-core', 0.5371223092079163, 'ml', 5), ('deepmind/deepmind-research', 0.5370939373970032, 'ml', 0), ('mlc-ai/mlc-llm', 0.5367917418479919, 'llm', 0), ('dmlc/dgl', 0.5359228849411011, 'ml-dl', 1), ('jina-ai/finetuner', 0.5358811616897583, 'ml', 0), ('iperov/deepfacelab', 0.5346398949623108, 'ml-dl', 1), ('bigscience-workshop/petals', 0.5345199704170227, 'data', 4), ('unity-technologies/ml-agents', 0.5343484878540039, 'ml-rl', 1), ('mrdbourke/pytorch-deep-learning', 0.5341781973838806, 'study', 2), ('jina-ai/clip-as-service', 0.5337973833084106, 'nlp', 2), ('ourownstory/neural_prophet', 0.5321094989776611, 'ml', 3), ('rasahq/rasa', 0.5313621163368225, 'llm', 1), ('tensorflow/addons', 0.5303577780723572, 'ml', 2), ('towhee-io/towhee', 0.5302903652191162, 'ml-ops', 1), ('xl0/lovely-tensors', 0.5299148559570312, 'ml-dl', 2), ('fepegar/torchio', 0.5291038155555725, 'ml-dl', 2), ('ggerganov/ggml', 0.5287749171257019, 'ml', 1), ('franck-dernoncourt/neuroner', 0.5285832285881042, 'nlp', 3), ('denys88/rl_games', 0.5283133387565613, 'ml-rl', 2), ('ashleve/lightning-hydra-template', 0.5269266963005066, 'util', 2), ('adap/flower', 0.5243560671806335, 'ml-ops', 3), ('luodian/otter', 0.52414870262146, 'llm', 1), ('huggingface/huggingface_hub', 0.5234879851341248, 'ml', 2), ('skorch-dev/skorch', 0.5229756832122803, 'ml-dl', 1), ('nvidia/nemo', 0.5229525566101074, 'nlp', 4), ('amanchadha/coursera-deep-learning-specialization', 0.5228648781776428, 'study', 1), ('ml-tooling/opyrator', 0.5223838090896606, 'viz', 0), ('pyro-ppl/pyro', 0.5212848782539368, 'ml-dl', 2), ('intellabs/bayesian-torch', 0.5206497311592102, 'ml', 2), ('mlflow/mlflow', 0.5205937027931213, 'ml-ops', 0), ('paddlepaddle/paddlenlp', 0.5180720090866089, 'llm', 1), ('aws/sagemaker-python-sdk', 0.5177861452102661, 'ml', 3), ('google-research/electra', 0.5167393684387207, 'ml-dl', 3), ('keras-rl/keras-rl', 0.5158306956291199, 'ml-rl', 1), ('roboflow/supervision', 0.5154585242271423, 'ml', 4), ('huggingface/text-generation-inference', 0.5152902007102966, 'llm', 3), ('titanml/takeoff', 0.5151104927062988, 'llm', 0), ('espnet/espnet', 0.5145514607429504, 'nlp', 4), ('lucidrains/imagen-pytorch', 0.5135546326637268, 'ml-dl', 1), ('google-research/deeplab2', 0.5127051472663879, 'ml', 0), ('deepfakes/faceswap', 0.5123569965362549, 'ml-dl', 1), ('davidadsp/generative_deep_learning_2nd_edition', 0.5120260119438171, 'study', 2), ('paddlepaddle/paddle', 0.5118424296379089, 'ml-dl', 2), ('minimaxir/textgenrnn', 0.5115391612052917, 'nlp', 2), ('deepmind/dm-haiku', 0.5113754868507385, 'ml-dl', 1), ('project-monai/monai', 0.5109444260597229, 'ml', 2), ('tensorly/tensorly', 0.5106650590896606, 'ml-dl', 3), ('tigerlab-ai/tiger', 0.5106365084648132, 'llm', 1), ('oegedijk/explainerdashboard', 0.5068669319152832, 'ml-interpretability', 0), ('huggingface/autotrain-advanced', 0.5064716339111328, 'ml', 1), ('nebuly-ai/nebullvm', 0.5064698457717896, 'perf', 1), ('udacity/deep-learning-v2-pytorch', 0.505573570728302, 'study', 2), ('rasbt/stat453-deep-learning-ss20', 0.5055111050605774, 'study', 0), ('koaning/embetter', 0.5039408206939697, 'data', 0), ('microsoft/unilm', 0.5029410719871521, 'nlp', 1), ('salesforce/blip', 0.5009772181510925, 'diffusion', 0), ('keras-team/keras-cv', 0.5008230805397034, 'ml-dl', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5003179907798767, 'study', 1)] | 115 | 1 | null | 1.15 | 11 | 0 | 69 | 1 | 0 | 0 | 0 | 11 | 3 | 90 | 0.3 | 48 |
115 | nlp | https://github.com/allenai/allennlp | [] | null | [] | [] | null | null | null | allenai/allennlp | allennlp | 11,631 | 2,261 | 284 | Python | http://www.allennlp.org | An open-source NLP research library, built on PyTorch. | allenai | 2024-01-13 | 2017-05-15 | 350 | 33.21787 | https://avatars.githubusercontent.com/u/5667695?v=4 | An open-source NLP research library, built on PyTorch. | ['data-science', 'deep-learning', 'natural-language-processing', 'nlp', 'pytorch'] | ['data-science', 'deep-learning', 'natural-language-processing', 'nlp', 'pytorch'] | 2022-11-22 | [('nltk/nltk', 0.6935926675796509, 'nlp', 2), ('alibaba/easynlp', 0.6890572309494019, 'nlp', 3), ('flairnlp/flair', 0.6880818605422974, 'nlp', 3), ('graykode/nlp-tutorial', 0.6779881715774536, 'study', 3), ('pytorch/ignite', 0.6625421047210693, 'ml-dl', 2), ('explosion/spacy', 0.6516764163970947, 'nlp', 4), ('skorch-dev/skorch', 0.639916181564331, 'ml-dl', 1), ('huggingface/transformers', 0.6358655095100403, 'nlp', 4), ('pemistahl/lingua-py', 0.6268466711044312, 'nlp', 2), ('explosion/spacy-models', 0.6203604340553284, 'nlp', 2), ('deeppavlov/deeppavlov', 0.6174921989440918, 'nlp', 2), ('rasahq/rasa', 0.6140093207359314, 'llm', 2), ('rasbt/machine-learning-book', 0.6064896583557129, 'study', 2), ('keras-team/keras-nlp', 0.605678379535675, 'nlp', 3), ('mrdbourke/pytorch-deep-learning', 0.6028923988342285, 'study', 2), ('norskregnesentral/skweak', 0.5997143387794495, 'nlp', 2), ('deepset-ai/farm', 0.5969370007514954, 'nlp', 3), ('intel/intel-extension-for-pytorch', 0.5950837731361389, 'perf', 2), ('ibm/transition-amr-parser', 0.5920513272285461, 'nlp', 1), ('pytorch/captum', 0.5909707546234131, 'ml-interpretability', 0), ('pytorch/data', 0.5899907946586609, 'data', 0), ('speechbrain/speechbrain', 0.5886344313621521, 'nlp', 2), ('pyg-team/pytorch_geometric', 0.5823215246200562, 'ml-dl', 2), ('franck-dernoncourt/neuroner', 0.5776329040527344, 'nlp', 2), ('infinitylogesh/mutate', 0.5738117098808289, 'nlp', 0), ('paddlepaddle/paddlenlp', 0.5729801058769226, 'llm', 1), ('google-research/electra', 0.5719739198684692, 'ml-dl', 2), ('salesforce/blip', 0.5719388127326965, 'diffusion', 0), ('huggingface/text-generation-inference', 0.5714684128761292, 'llm', 3), ('nvidia/apex', 0.5706870555877686, 'ml-dl', 0), ('koaning/whatlies', 0.570354700088501, 'nlp', 1), ('nvidia/deeplearningexamples', 0.5669660568237305, 'ml-dl', 3), ('timdettmers/bitsandbytes', 0.5652725100517273, 'util', 0), ('pytorch/rl', 0.5637892484664917, 'ml-rl', 1), ('explosion/thinc', 0.5593048930168152, 'ml-dl', 4), ('lucidrains/imagen-pytorch', 0.5545222163200378, 'ml-dl', 1), ('explosion/spacy-llm', 0.5512605905532837, 'llm', 2), ('jalammar/ecco', 0.5496200323104858, 'ml-interpretability', 3), ('bytedance/lightseq', 0.5493444800376892, 'nlp', 0), ('openlmlab/moss', 0.548026978969574, 'llm', 2), ('databrickslabs/dolly', 0.5456701517105103, 'llm', 0), ('denys88/rl_games', 0.5432136654853821, 'ml-rl', 2), ('tensorflow/tensor2tensor', 0.5407803058624268, 'ml', 1), ('ashleve/lightning-hydra-template', 0.5402539968490601, 'util', 2), ('nvidia/nemo', 0.5374099016189575, 'nlp', 2), ('argilla-io/argilla', 0.5366323590278625, 'nlp', 2), ('lucidrains/dalle2-pytorch', 0.5331498384475708, 'diffusion', 1), ('llmware-ai/llmware', 0.5309963226318359, 'llm', 2), ('blinkdl/chatrwkv', 0.5303969979286194, 'llm', 1), ('huggingface/huggingface_hub', 0.5300591588020325, 'ml', 3), ('plasticityai/magnitude', 0.5297778248786926, 'nlp', 2), ('rentruewang/koila', 0.5262885689735413, 'ml', 2), ('makcedward/nlpaug', 0.5257618427276611, 'nlp', 3), ('cqcl/lambeq', 0.5252924561500549, 'nlp', 0), ('lightly-ai/lightly', 0.5244608521461487, 'ml', 2), ('lexpredict/lexpredict-lexnlp', 0.524100661277771, 'nlp', 1), ('sloria/textblob', 0.5219146013259888, 'nlp', 2), ('huggingface/datasets', 0.5202730894088745, 'nlp', 4), ('google-research/language', 0.5185959935188293, 'nlp', 1), ('optimalscale/lmflow', 0.5176029801368713, 'llm', 2), ('deepmind/deepmind-research', 0.514882504940033, 'ml', 0), ('karpathy/micrograd', 0.5140243768692017, 'study', 0), ('minimaxir/textgenrnn', 0.5124790072441101, 'nlp', 1), ('jbesomi/texthero', 0.5118191242218018, 'nlp', 1), ('tensorlayer/tensorlayer', 0.5114910006523132, 'ml-rl', 1), ('minimaxir/gpt-2-simple', 0.510489821434021, 'llm', 0), ('blackhc/toma', 0.5094995498657227, 'ml-dl', 2), ('aiwaves-cn/agents', 0.5092148184776306, 'nlp', 0), ('ferdinandzhong/punctuator', 0.5085445046424866, 'nlp', 3), ('neuml/txtai', 0.50567227602005, 'nlp', 1), ('thu-ml/tianshou', 0.5055130124092102, 'ml-rl', 1), ('cgpotts/cs224u', 0.5052735805511475, 'study', 1), ('extreme-bert/extreme-bert', 0.5051680207252502, 'llm', 4), ('faster-cpython/ideas', 0.5050787329673767, 'perf', 0), ('mooler0410/llmspracticalguide', 0.5047245025634766, 'study', 2), ('lm-sys/fastchat', 0.5036823153495789, 'llm', 0), ('xl0/lovely-tensors', 0.5030158162117004, 'ml-dl', 2), ('carla-recourse/carla', 0.5022084712982178, 'ml', 1), ('bigscience-workshop/promptsource', 0.5015190839767456, 'nlp', 2), ('koaning/human-learn', 0.501263439655304, 'data', 0)] | 267 | 5 | null | 0 | 0 | 0 | 81 | 14 | 0 | 10 | 10 | 0 | 0 | 90 | 0 | 48 |
1,827 | util | https://github.com/zulko/moviepy | [] | null | [] | [] | null | null | null | zulko/moviepy | moviepy | 11,300 | 1,501 | 256 | Python | https://zulko.github.io/moviepy/ | Video editing with Python | zulko | 2024-01-14 | 2013-08-12 | 546 | 20.690557 | null | Video editing with Python | ['animation', 'gif', 'video', 'video-editing', 'video-processing'] | ['animation', 'gif', 'video', 'video-editing', 'video-processing'] | 2023-07-11 | [('imageio/imageio', 0.6167894601821899, 'util', 1), ('soft-matter/pims', 0.6140278577804565, 'util', 1), ('scikit-image/scikit-image', 0.5875822305679321, 'util', 0), ('chenyangqiqi/fatezero', 0.5514541268348694, 'diffusion', 1), ('researchmm/sttn', 0.5262514352798462, 'ml-dl', 0), ('has2k1/plotnine', 0.524414598941803, 'viz', 0), ('pyglet/pyglet', 0.5143889784812927, 'gamedev', 0), ('renpy/renpy', 0.5059114098548889, 'viz', 0), ('open-mmlab/mmediting', 0.5014966726303101, 'ml', 0)] | 160 | 1 | null | 0.21 | 85 | 22 | 127 | 6 | 0 | 1 | 1 | 85 | 137 | 90 | 1.6 | 48 |
215 | debug | https://github.com/gruns/icecream | [] | null | [] | [] | null | null | null | gruns/icecream | icecream | 8,090 | 171 | 50 | Python | null | 🍦 Never use print() to debug again. | gruns | 2024-01-13 | 2018-02-13 | 311 | 26.012862 | null | 🍦 Never use print() to debug again. | ['debug', 'debugging', 'debugging-tool', 'inspects', 'print'] | ['debug', 'debugging', 'debugging-tool', 'inspects', 'print'] | 2022-12-04 | [('cool-rr/pysnooper', 0.8130260705947876, 'debug', 1)] | 21 | 7 | null | 0 | 26 | 4 | 72 | 14 | 0 | 1 | 1 | 26 | 38 | 90 | 1.5 | 48 |
1,206 | ml | https://github.com/uberi/speech_recognition | [] | null | [] | [] | null | null | null | uberi/speech_recognition | speech_recognition | 7,796 | 2,331 | 282 | Python | https://pypi.python.org/pypi/SpeechRecognition/ | Speech recognition module for Python, supporting several engines and APIs, online and offline. | uberi | 2024-01-14 | 2014-04-23 | 509 | 15.290558 | null | Speech recognition module for Python, supporting several engines and APIs, online and offline. | ['audio', 'speech-recognition', 'speech-to-text'] | ['audio', 'speech-recognition', 'speech-to-text'] | 2023-12-06 | [('pndurette/gtts', 0.7022308707237244, 'util', 0), ('nateshmbhat/pyttsx3', 0.6923384070396423, 'util', 0), ('spotify/pedalboard', 0.6759282946586609, 'util', 1), ('speechbrain/speechbrain', 0.6739375591278076, 'nlp', 3), ('irmen/pyminiaudio', 0.659464955329895, 'util', 0), ('googleapis/python-speech', 0.6549380421638489, 'ml', 0), ('pemistahl/lingua-py', 0.6296498775482178, 'nlp', 0), ('m-bain/whisperx', 0.6138349771499634, 'nlp', 2), ('espnet/espnet', 0.6095865964889526, 'nlp', 1), ('bastibe/python-soundfile', 0.575599730014801, 'util', 0), ('taylorsmarks/playsound', 0.5477955341339111, 'util', 0), ('jamesturk/jellyfish', 0.545852541923523, 'nlp', 0), ('minimaxir/simpleaichat', 0.5369465351104736, 'llm', 0), ('quodlibet/mutagen', 0.5264820456504822, 'util', 0), ('pytoolz/toolz', 0.517691969871521, 'util', 0), ('clips/pattern', 0.5162601470947266, 'nlp', 0), ('cmusphinx/pocketsphinx', 0.5127110481262207, 'ml', 1), ('deeppavlov/deeppavlov', 0.509485125541687, 'nlp', 0), ('pypy/pypy', 0.5066007971763611, 'util', 0), ('rasbt/mlxtend', 0.5056686401367188, 'ml', 0), ('rasahq/rasa', 0.5011873245239258, 'llm', 0)] | 49 | 4 | null | 0.98 | 45 | 14 | 118 | 1 | 2 | 5 | 2 | 45 | 36 | 90 | 0.8 | 48 |
132 | ml | https://github.com/automl/auto-sklearn | [] | null | [] | [] | null | null | null | automl/auto-sklearn | auto-sklearn | 7,287 | 1,268 | 215 | Python | https://automl.github.io/auto-sklearn | Automated Machine Learning with scikit-learn | automl | 2024-01-13 | 2015-07-02 | 447 | 16.276005 | https://avatars.githubusercontent.com/u/6469053?v=4 | Automated Machine Learning with scikit-learn | ['automated-machine-learning', 'automl', 'bayesian-optimization', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'meta-learning', 'metalearning', 'scikit-learn', 'smac'] | ['automated-machine-learning', 'automl', 'bayesian-optimization', 'hyperparameter-optimization', 'hyperparameter-search', 'hyperparameter-tuning', 'meta-learning', 'metalearning', 'scikit-learn', 'smac'] | 2023-04-18 | [('microsoft/nni', 0.7624291777610779, 'ml', 5), ('nccr-itmo/fedot', 0.7373944520950317, 'ml-ops', 3), ('microsoft/flaml', 0.7281423807144165, 'ml', 4), ('ray-project/tune-sklearn', 0.6818026304244995, 'ml', 4), ('winedarksea/autots', 0.6762388944625854, 'time-series', 1), ('awslabs/autogluon', 0.672629177570343, 'ml', 4), ('mljar/mljar-supervised', 0.6622180938720703, 'ml', 4), ('epistasislab/tpot', 0.6574358940124512, 'ml', 4), ('featurelabs/featuretools', 0.6524330377578735, 'ml', 3), ('districtdatalabs/yellowbrick', 0.6410435438156128, 'ml', 1), ('google/pyglove', 0.6234724521636963, 'util', 2), ('keras-team/autokeras', 0.616755485534668, 'ml-dl', 2), ('koaning/scikit-lego', 0.6153001189231873, 'ml', 1), ('scikit-optimize/scikit-optimize', 0.6117300391197205, 'ml', 5), ('scikit-learn/scikit-learn', 0.6095296144485474, 'ml', 0), ('determined-ai/determined', 0.6021798253059387, 'ml-ops', 3), ('rasbt/machine-learning-book', 0.6004539132118225, 'study', 1), ('patchy631/machine-learning', 0.6001100540161133, 'ml', 0), ('skops-dev/skops', 0.5991626381874084, 'ml-ops', 1), ('sktime/sktime', 0.5886750817298889, 'time-series', 1), ('intel/scikit-learn-intelex', 0.5883508920669556, 'perf', 1), ('koaning/human-learn', 0.5842374563217163, 'data', 1), ('scikit-learn-contrib/metric-learn', 0.576133131980896, 'ml', 1), ('shankarpandala/lazypredict', 0.5726633667945862, 'ml', 1), ('xplainable/xplainable', 0.5678261518478394, 'ml-interpretability', 0), ('gradio-app/gradio', 0.5600031614303589, 'viz', 0), ('onnx/onnx', 0.5575015544891357, 'ml', 1), ('huggingface/autotrain-advanced', 0.5570406317710876, 'ml', 0), ('iryna-kondr/scikit-llm', 0.5549347996711731, 'llm', 1), ('fatiando/verde', 0.5549225807189941, 'gis', 0), ('polyaxon/polyaxon', 0.5545597672462463, 'ml-ops', 1), ('firmai/atspy', 0.5523808598518372, 'time-series', 0), ('google/vizier', 0.5519843101501465, 'ml', 3), ('optuna/optuna', 0.5508047938346863, 'ml', 1), ('firmai/industry-machine-learning', 0.5466862320899963, 'study', 0), ('ml-tooling/opyrator', 0.546398937702179, 'viz', 0), ('huggingface/evaluate', 0.5426995158195496, 'ml', 0), ('wandb/client', 0.5397332906723022, 'ml', 3), ('teamhg-memex/eli5', 0.5375847220420837, 'ml', 1), ('csinva/imodels', 0.5375404953956604, 'ml', 1), ('mlflow/mlflow', 0.5365729928016663, 'ml-ops', 0), ('alpa-projects/alpa', 0.535767674446106, 'ml-dl', 0), ('ageron/handson-ml2', 0.531454861164093, 'ml', 0), ('mosaicml/composer', 0.5304707288742065, 'ml-dl', 0), ('eugeneyan/testing-ml', 0.528367280960083, 'testing', 0), ('huggingface/datasets', 0.5262554883956909, 'nlp', 0), ('online-ml/river', 0.5197332501411438, 'ml', 0), ('kubeflow/pipelines', 0.5186381936073303, 'ml-ops', 0), ('ddbourgin/numpy-ml', 0.5156731605529785, 'ml', 0), ('kubeflow/katib', 0.5148612260818481, 'ml', 0), ('tensorflow/tensorflow', 0.5126420259475708, 'ml-dl', 0), ('alirezadir/machine-learning-interview-enlightener', 0.5101978778839111, 'study', 0), ('rasbt/mlxtend', 0.5089040994644165, 'ml', 0), ('dask/dask-ml', 0.5055193305015564, 'ml', 0), ('google-research/google-research', 0.5043892860412598, 'ml', 0), ('ray-project/ray', 0.5039158463478088, 'ml-ops', 3), ('ourownstory/neural_prophet', 0.5033456683158875, 'ml', 0), ('tensorflow/data-validation', 0.5028933882713318, 'ml-ops', 0), ('unionai-oss/unionml', 0.5017037987709045, 'ml-ops', 0)] | 88 | 7 | null | 0.02 | 32 | 12 | 104 | 9 | 1 | 4 | 1 | 32 | 50 | 90 | 1.6 | 48 |
594 | gis | https://github.com/domlysz/blendergis | [] | null | [] | [] | null | null | null | domlysz/blendergis | BlenderGIS | 6,989 | 1,302 | 257 | Python | null | Blender addons to make the bridge between Blender and geographic data | domlysz | 2024-01-13 | 2014-05-08 | 507 | 13.765616 | null | Blender addons to make the bridge between Blender and geographic data | ['3d', '3d-map', '3dgis', 'addon', 'basemap', 'blender', 'delaunay-triangulation', 'dem', 'geodata', 'georeferencing', 'geospatial', 'gis', 'importexport', 'map', 'openstreetmap', 'raster', 'shapefile', 'terrain-model'] | ['3d', '3d-map', '3dgis', 'addon', 'basemap', 'blender', 'delaunay-triangulation', 'dem', 'geodata', 'georeferencing', 'geospatial', 'gis', 'importexport', 'map', 'openstreetmap', 'raster', 'shapefile', 'terrain-model'] | 2024-01-08 | [('raphaelquast/eomaps', 0.5599436163902283, 'gis', 2), ('darribas/gds_env', 0.5147411823272705, 'gis', 0), ('isl-org/open3d', 0.5055702924728394, 'sim', 1)] | 15 | 3 | null | 0.02 | 49 | 9 | 118 | 0 | 0 | 2 | 2 | 49 | 81 | 90 | 1.7 | 48 |
637 | util | https://github.com/theskumar/python-dotenv | [] | null | [] | [] | null | null | null | theskumar/python-dotenv | python-dotenv | 6,790 | 434 | 34 | Python | https://saurabh-kumar.com/python-dotenv/ | Reads key-value pairs from a .env file and can set them as environment variables. It helps in developing applications following the 12-factor principles. | theskumar | 2024-01-14 | 2014-09-06 | 490 | 13.845033 | null | Reads key-value pairs from a .env file and can set them as environment variables. It helps in developing applications following the 12-factor principles. | ['12-factor-app', 'configuration', 'devops-tools', 'dotenv', 'env', 'environment-variables'] | ['12-factor-app', 'configuration', 'devops-tools', 'dotenv', 'env', 'environment-variables'] | 2023-07-07 | [] | 93 | 6 | null | 0.17 | 17 | 9 | 114 | 6 | 2 | 5 | 2 | 17 | 34 | 90 | 2 | 48 |
223 | nlp | https://github.com/kingoflolz/mesh-transformer-jax | [] | null | [] | [] | 1 | null | null | kingoflolz/mesh-transformer-jax | mesh-transformer-jax | 6,165 | 897 | 109 | Python | null | Model parallel transformers in JAX and Haiku | kingoflolz | 2024-01-13 | 2021-03-13 | 150 | 40.982906 | null | Model parallel transformers in JAX and Haiku | [] | [] | 2023-01-12 | [('huggingface/transformers', 0.5089766979217529, 'nlp', 0)] | 23 | 3 | null | 0 | 7 | 0 | 35 | 12 | 0 | 0 | 0 | 7 | 18 | 90 | 2.6 | 48 |
437 | perf | https://github.com/mher/flower | [] | null | [] | [] | null | null | null | mher/flower | flower | 6,007 | 1,046 | 143 | Python | https://flower.readthedocs.io | Real-time monitor and web admin for Celery distributed task queue | mher | 2024-01-14 | 2012-07-08 | 603 | 9.957139 | null | Real-time monitor and web admin for Celery distributed task queue | ['administration', 'asynchronous', 'celery', 'monitoring', 'rabbitmq', 'redis', 'task-queue', 'workers'] | ['administration', 'asynchronous', 'celery', 'monitoring', 'rabbitmq', 'redis', 'task-queue', 'workers'] | 2023-12-17 | [('celery/celery', 0.6660754680633545, 'perf', 1), ('bogdanp/dramatiq', 0.5586143732070923, 'util', 1), ('samuelcolvin/arq', 0.5143932104110718, 'data', 1), ('sumerc/yappi', 0.5045316219329834, 'profiling', 1), ('airtai/faststream', 0.5014379620552063, 'perf', 2)] | 211 | 6 | null | 1.08 | 29 | 11 | 140 | 1 | 0 | 2 | 2 | 29 | 26 | 90 | 0.9 | 48 |
1,387 | security | https://github.com/nccgroup/scoutsuite | [] | null | [] | [] | null | null | null | nccgroup/scoutsuite | ScoutSuite | 5,923 | 980 | 125 | Python | null | Multi-Cloud Security Auditing Tool | nccgroup | 2024-01-13 | 2018-10-30 | 274 | 21.616788 | https://avatars.githubusercontent.com/u/4067082?v=4 | Multi-Cloud Security Auditing Tool | ['auditing', 'aws', 'azure', 'cloud', 'gcp', 'security'] | ['auditing', 'aws', 'azure', 'cloud', 'gcp', 'security'] | 2023-09-22 | [('jorgebastida/awslogs', 0.5164755582809448, 'util', 0)] | 119 | 3 | null | 0.81 | 27 | 3 | 63 | 4 | 3 | 13 | 3 | 27 | 18 | 90 | 0.7 | 48 |
566 | util | https://github.com/jd/tenacity | [] | null | [] | [] | 1 | null | null | jd/tenacity | tenacity | 5,616 | 285 | 48 | Python | http://tenacity.readthedocs.io | Retrying library for Python | jd | 2024-01-14 | 2016-08-11 | 389 | 14.410557 | null | Retrying library for Python | ['failure', 'retry', 'retry-library'] | ['failure', 'retry', 'retry-library'] | 2023-12-18 | [] | 87 | 3 | null | 0.56 | 17 | 8 | 90 | 1 | 0 | 9 | 9 | 17 | 12 | 90 | 0.7 | 48 |
600 | jupyter | https://github.com/connorferster/handcalcs | [] | null | [] | [] | null | null | null | connorferster/handcalcs | handcalcs | 5,300 | 444 | 84 | CSS | null | Python library for converting Python calculations into rendered latex. | connorferster | 2024-01-12 | 2020-02-19 | 205 | 25.74601 | null | Python library for converting Python calculations into rendered latex. | [] | [] | 2023-11-12 | [('google/latexify_py', 0.7623890042304993, 'util', 0), ('pytoolz/toolz', 0.6247959733009338, 'util', 0), ('pypy/pypy', 0.5907471179962158, 'util', 0), ('fredrik-johansson/mpmath', 0.5756496787071228, 'math', 0), ('pyscf/pyscf', 0.5601664185523987, 'sim', 0), ('sympy/sympy', 0.5598889589309692, 'math', 0), ('pyston/pyston', 0.5578861832618713, 'util', 0), ('wtforms/wtforms', 0.5505093336105347, 'web', 0), ('eleutherai/pyfra', 0.5502340793609619, 'ml', 0), ('zoomeranalytics/xlwings', 0.5488008856773376, 'data', 0), ('python/cpython', 0.5454192161560059, 'util', 0), ('julienpalard/pipe', 0.5392968058586121, 'util', 0), ('pmorissette/ffn', 0.5378669500350952, 'finance', 0), ('numpy/numpy', 0.5350318551063538, 'math', 0), ('imageio/imageio', 0.5331127047538757, 'util', 0), ('altair-viz/altair', 0.531620442867279, 'viz', 0), ('google/yapf', 0.5293661952018738, 'util', 0), ('hhatto/autopep8', 0.5234463810920715, 'util', 0), ('maartenbreddels/ipyvolume', 0.5224195122718811, 'jupyter', 0), ('pyglet/pyglet', 0.5195719003677368, 'gamedev', 0), ('jmcnamara/xlsxwriter', 0.5153928399085999, 'data', 0), ('r0x0r/pywebview', 0.5135572552680969, 'gui', 0), ('gbeced/pyalgotrade', 0.5120189189910889, 'finance', 0), ('beeware/briefcase', 0.5107748508453369, 'util', 0), ('webpy/webpy', 0.5106810331344604, 'web', 0), ('sqlalchemy/mako', 0.5080623626708984, 'template', 0), ('hgrecco/pint', 0.506892740726471, 'util', 0), ('cython/cython', 0.5067312121391296, 'util', 0), ('google/jax', 0.5066884160041809, 'ml', 0), ('dfki-ric/pytransform3d', 0.5065004229545593, 'math', 0), ('urwid/urwid', 0.5055436491966248, 'term', 0), ('python-markdown/markdown', 0.5013054609298706, 'util', 0), ('pyfpdf/fpdf2', 0.5012051463127136, 'util', 0), ('pympler/pympler', 0.5004320740699768, 'perf', 0)] | 11 | 5 | null | 0.04 | 9 | 0 | 48 | 2 | 0 | 2 | 2 | 9 | 14 | 90 | 1.6 | 48 |
1,868 | util | https://github.com/pdfminer/pdfminer.six | [] | null | [] | [] | null | null | null | pdfminer/pdfminer.six | pdfminer.six | 5,083 | 863 | 121 | Python | https://pdfminersix.readthedocs.io | Community maintained fork of pdfminer - we fathom PDF | pdfminer | 2024-01-13 | 2014-08-29 | 491 | 10.340308 | https://avatars.githubusercontent.com/u/22586632?v=4 | Community maintained fork of pdfminer - we fathom PDF | ['parser', 'pdf'] | ['parser', 'pdf'] | 2024-01-12 | [('py-pdf/pypdf2', 0.5491688847541809, 'util', 1), ('pyfpdf/fpdf2', 0.5335804224014282, 'util', 1), ('allenai/s2orc-doc2json', 0.5287355184555054, 'nlp', 0), ('unstructured-io/pipeline-paddleocr', 0.5148302316665649, 'data', 1)] | 137 | 4 | null | 0.35 | 62 | 37 | 114 | 0 | 1 | 2 | 1 | 62 | 74 | 90 | 1.2 | 48 |
102 | ml | https://github.com/rasbt/mlxtend | [] | null | [] | [] | null | null | null | rasbt/mlxtend | mlxtend | 4,676 | 839 | 117 | Python | https://rasbt.github.io/mlxtend/ | A library of extension and helper modules for Python's data analysis and machine learning libraries. | rasbt | 2024-01-13 | 2014-08-14 | 493 | 9.471065 | null | A library of extension and helper modules for Python's data analysis and machine learning libraries. | ['association-rules', 'data-mining', 'data-science', 'machine-learning', 'supervised-learning', 'unsupervised-learning'] | ['association-rules', 'data-mining', 'data-science', 'machine-learning', 'supervised-learning', 'unsupervised-learning'] | 2024-01-05 | [('scikit-learn/scikit-learn', 0.7584848403930664, 'ml', 2), ('pycaret/pycaret', 0.7207165956497192, 'ml', 2), ('featurelabs/featuretools', 0.6914775371551514, 'ml', 2), ('tensorflow/data-validation', 0.6768656373023987, 'ml-ops', 0), ('gradio-app/gradio', 0.6547028422355652, 'viz', 2), ('scikit-learn-contrib/imbalanced-learn', 0.651149570941925, 'ml', 2), ('pandas-dev/pandas', 0.6480407118797302, 'pandas', 1), ('clips/pattern', 0.6350256204605103, 'nlp', 1), ('alkaline-ml/pmdarima', 0.6218876838684082, 'time-series', 1), ('jovianml/opendatasets', 0.6177619695663452, 'data', 2), ('huggingface/evaluate', 0.6174339056015015, 'ml', 1), ('krzjoa/awesome-python-data-science', 0.610371470451355, 'study', 2), ('dylanhogg/awesome-python', 0.6053752899169922, 'study', 2), ('yzhao062/pyod', 0.6038527488708496, 'data', 4), ('lightly-ai/lightly', 0.6017403602600098, 'ml', 1), ('pytoolz/toolz', 0.6010292172431946, 'util', 0), ('scikit-learn-contrib/metric-learn', 0.6002252101898193, 'ml', 1), ('ta-lib/ta-lib-python', 0.5990067720413208, 'finance', 0), ('wesm/pydata-book', 0.5947810411453247, 'study', 0), ('google/temporian', 0.58907151222229, 'time-series', 0), ('epistasislab/tpot', 0.5866554379463196, 'ml', 2), ('pyeve/cerberus', 0.5806938409805298, 'data', 0), ('firmai/industry-machine-learning', 0.5761663913726807, 'study', 2), ('probml/pyprobml', 0.5730016827583313, 'ml', 1), ('mljar/mljar-supervised', 0.5722818374633789, 'ml', 2), ('online-ml/river', 0.5713738799095154, 'ml', 2), ('mdbloice/augmentor', 0.5698684453964233, 'ml', 1), ('nicolashug/surprise', 0.5660281777381897, 'ml', 1), ('polyaxon/datatile', 0.5656301975250244, 'pandas', 1), ('ageron/handson-ml2', 0.564663290977478, 'ml', 0), ('districtdatalabs/yellowbrick', 0.5595728754997253, 'ml', 1), ('eleutherai/pyfra', 0.5588405728340149, 'ml', 0), ('scikit-learn-contrib/lightning', 0.5572295188903809, 'ml', 1), ('tdameritrade/stumpy', 0.5554875135421753, 'time-series', 1), ('merantix-momentum/squirrel-core', 0.5547300577163696, 'ml', 2), ('teamhg-memex/eli5', 0.553990364074707, 'ml', 2), ('sktime/sktime', 0.5530063509941101, 'time-series', 3), ('firmai/atspy', 0.5510342121124268, 'time-series', 0), ('ggerganov/ggml', 0.5479773283004761, 'ml', 1), ('sentinel-hub/eo-learn', 0.5460903644561768, 'gis', 1), ('pemistahl/lingua-py', 0.5422750115394592, 'nlp', 0), ('selfexplainml/piml-toolbox', 0.5372369885444641, 'ml-interpretability', 0), ('unit8co/darts', 0.5370567440986633, 'time-series', 2), ('microsoft/flaml', 0.5349463224411011, 'ml', 2), ('rasbt/machine-learning-book', 0.5330138206481934, 'study', 1), ('gerdm/prml', 0.5303501486778259, 'study', 1), ('dagworks-inc/hamilton', 0.5286049842834473, 'ml-ops', 2), ('carla-recourse/carla', 0.5272185802459717, 'ml', 1), ('goldmansachs/gs-quant', 0.5270822644233704, 'finance', 0), ('pyutils/line_profiler', 0.5252503156661987, 'profiling', 0), ('catboost/catboost', 0.5251424908638, 'ml', 3), ('patchy631/machine-learning', 0.5243169665336609, 'ml', 0), ('scikit-mobility/scikit-mobility', 0.5235726833343506, 'gis', 1), ('sympy/sympy', 0.5225716829299927, 'math', 0), ('statsmodels/statsmodels', 0.5218582153320312, 'ml', 1), ('quantecon/quantecon.py', 0.5213393568992615, 'sim', 0), ('altair-viz/altair', 0.5204195976257324, 'viz', 0), ('ddbourgin/numpy-ml', 0.5202142000198364, 'ml', 1), ('mckinsey/causalnex', 0.5194756984710693, 'math', 2), ('pysal/pysal', 0.5194495320320129, 'gis', 0), ('huggingface/datasets', 0.5176108479499817, 'nlp', 1), ('csinva/imodels', 0.5165061354637146, 'ml', 3), ('xrudelis/pytrait', 0.5159803628921509, 'util', 0), ('pythonspeed/filprofiler', 0.5145261883735657, 'profiling', 0), ('kubeflow/fairing', 0.5129528045654297, 'ml-ops', 0), ('unionai-oss/pandera', 0.5126389265060425, 'pandas', 0), ('tensorly/tensorly', 0.5090686082839966, 'ml-dl', 1), ('automl/auto-sklearn', 0.5089040994644165, 'ml', 0), ('jakevdp/pythondatasciencehandbook', 0.5061179399490356, 'study', 0), ('uberi/speech_recognition', 0.5056686401367188, 'ml', 0), ('stan-dev/pystan', 0.5054618716239929, 'ml', 0), ('sloria/textblob', 0.5048877596855164, 'nlp', 0), ('marcomusy/vedo', 0.5048307180404663, 'viz', 0), ('keon/algorithms', 0.5041128993034363, 'util', 0), ('scitools/iris', 0.5022916793823242, 'gis', 0), ('weecology/deepforest', 0.5020350217819214, 'gis', 0), ('guyallard/markov_clustering', 0.5017951130867004, 'graph', 0), ('skorch-dev/skorch', 0.5017459988594055, 'ml-dl', 1), ('pyparsing/pyparsing', 0.5005236864089966, 'util', 0), ('scipy/scipy', 0.5000782012939453, 'math', 0)] | 103 | 5 | null | 1.08 | 7 | 4 | 115 | 0 | 3 | 3 | 3 | 7 | 11 | 90 | 1.6 | 48 |
1,813 | study | https://github.com/mrdbourke/tensorflow-deep-learning | [] | null | [] | [] | null | null | null | mrdbourke/tensorflow-deep-learning | tensorflow-deep-learning | 4,631 | 2,271 | 149 | Jupyter Notebook | https://dbourke.link/ZTMTFcourse | All course materials for the Zero to Mastery Deep Learning with TensorFlow course. | mrdbourke | 2024-01-13 | 2020-11-23 | 166 | 27.873603 | null | All course materials for the Zero to Mastery Deep Learning with TensorFlow course. | ['curriculum', 'deep-learning', 'deep-neural-networks', 'tensorflow', 'tensorflow-course', 'tensorflow-tutorials', 'tensorflow2'] | ['curriculum', 'deep-learning', 'deep-neural-networks', 'tensorflow', 'tensorflow-course', 'tensorflow-tutorials', 'tensorflow2'] | 2023-11-09 | [('mrdbourke/zero-to-mastery-ml', 0.7862590551376343, 'study', 1), ('mrdbourke/pytorch-deep-learning', 0.7342724800109863, 'study', 1), ('amanchadha/coursera-deep-learning-specialization', 0.592336118221283, 'study', 1), ('tensorlayer/tensorlayer', 0.576848566532135, 'ml-rl', 3), ('d2l-ai/d2l-en', 0.5674564242362976, 'study', 2), ('udacity/deep-learning-v2-pytorch', 0.5660417079925537, 'study', 1), ('tensorly/tensorly', 0.5285704731941223, 'ml-dl', 1), ('christoschristofidis/awesome-deep-learning', 0.5140354037284851, 'study', 1), ('keras-team/keras', 0.5046241283416748, 'ml-dl', 2), ('google/tf-quant-finance', 0.5032038688659668, 'finance', 1), ('keras-rl/keras-rl', 0.5023423433303833, 'ml-rl', 1), ('nyandwi/modernconvnets', 0.5007387399673462, 'ml-dl', 1)] | 27 | 3 | null | 0.73 | 11 | 0 | 38 | 2 | 0 | 0 | 0 | 11 | 23 | 90 | 2.1 | 48 |
1,626 | typing | https://github.com/instagram/monkeytype | ['static', 'annotation', 'runtime', 'code-quality'] | null | [] | [] | 1 | null | null | instagram/monkeytype | MonkeyType | 4,445 | 168 | 54 | Python | null | A Python library that generates static type annotations by collecting runtime types | instagram | 2024-01-13 | 2017-07-11 | 342 | 12.997076 | https://avatars.githubusercontent.com/u/549085?v=4 | A Python library that generates static type annotations by collecting runtime types | [] | ['annotation', 'code-quality', 'runtime', 'static'] | 2024-01-13 | [('google/pytype', 0.8035555481910706, 'typing', 1), ('microsoft/pyright', 0.7293500900268555, 'typing', 1), ('python/typeshed', 0.723702609539032, 'typing', 1), ('python/mypy', 0.7129145264625549, 'typing', 1), ('facebook/pyre-check', 0.6643034815788269, 'typing', 1), ('agronholm/typeguard', 0.6599376201629639, 'typing', 1), ('patrick-kidger/torchtyping', 0.6300653219223022, 'typing', 0), ('astral-sh/ruff', 0.6186012029647827, 'util', 1), ('strawberry-graphql/strawberry', 0.6149357557296753, 'web', 0), ('pytoolz/toolz', 0.5914798974990845, 'util', 0), ('psf/black', 0.5771250128746033, 'util', 1), ('landscapeio/prospector', 0.5743587017059326, 'util', 0), ('grantjenks/blue', 0.5733786225318909, 'util', 1), ('crytic/slither', 0.5720254182815552, 'crypto', 0), ('marshmallow-code/marshmallow', 0.5554887056350708, 'util', 0), ('xrudelis/pytrait', 0.5478743314743042, 'util', 0), ('google/latexify_py', 0.5287166237831116, 'util', 0), ('numba/llvmlite', 0.5182361602783203, 'util', 0), ('google/yapf', 0.5119272470474243, 'util', 1), ('rubik/radon', 0.5105746984481812, 'util', 0), ('getpelican/pelican', 0.5085812211036682, 'web', 0), ('instagram/libcst', 0.5069942474365234, 'util', 0), ('pyutils/line_profiler', 0.5013983845710754, 'profiling', 0)] | 48 | 5 | null | 0.27 | 18 | 12 | 79 | 0 | 0 | 3 | 3 | 18 | 19 | 90 | 1.1 | 48 |
711 | data | https://github.com/jazzband/tablib | [] | null | [] | [] | null | null | null | jazzband/tablib | tablib | 4,426 | 588 | 137 | Python | https://tablib.readthedocs.io/ | Python Module for Tabular Datasets in XLS, CSV, JSON, YAML, &c. | jazzband | 2024-01-13 | 2011-03-28 | 670 | 6.604562 | https://avatars.githubusercontent.com/u/15129049?v=4 | Python Module for Tabular Datasets in XLS, CSV, JSON, YAML, &c. | [] | [] | 2024-01-01 | [('astanin/python-tabulate', 0.6442596316337585, 'util', 0), ('zoomeranalytics/xlwings', 0.5972012877464294, 'data', 0), ('jmcnamara/xlsxwriter', 0.5911999344825745, 'data', 0), ('wireservice/csvkit', 0.5830636620521545, 'util', 0), ('vaexio/vaex', 0.575348973274231, 'perf', 0), ('saulpw/visidata', 0.5593103170394897, 'term', 0), ('python-odin/odin', 0.5585665106773376, 'util', 0), ('tkrabel/bamboolib', 0.5476372241973877, 'pandas', 0), ('camelot-dev/camelot', 0.5369576811790466, 'util', 0), ('mljar/mljar-supervised', 0.531550407409668, 'ml', 0), ('wesm/pydata-book', 0.5241658687591553, 'study', 0), ('koaning/drawdata', 0.5231077075004578, 'jupyter', 0), ('tiangolo/sqlmodel', 0.5218853950500488, 'data', 0), ('quantopian/qgrid', 0.5186475515365601, 'jupyter', 0), ('sqlalchemy/sqlalchemy', 0.5141265392303467, 'data', 0), ('bloomberg/ipydatagrid', 0.5118611454963684, 'jupyter', 0), ('jakevdp/pythondatasciencehandbook', 0.5034573078155518, 'study', 0), ('scikit-hep/awkward-1.0', 0.5033981800079346, 'data', 0), ('jazzband/prettytable', 0.5020918250083923, 'term', 0)] | 125 | 8 | null | 0.63 | 15 | 14 | 156 | 0 | 2 | 4 | 2 | 15 | 32 | 90 | 2.1 | 48 |
786 | ml-ops | https://github.com/orchest/orchest | [] | null | [] | [] | null | null | null | orchest/orchest | orchest | 4,005 | 253 | 43 | TypeScript | https://orchest.readthedocs.io/en/stable/ | Build data pipelines, the easy way 🛠️ | orchest | 2024-01-14 | 2020-05-21 | 192 | 20.782061 | https://avatars.githubusercontent.com/u/62945975?v=4 | Build data pipelines, the easy way 🛠️ | ['airflow', 'cloud', 'dag', 'data-pipelines', 'data-science', 'deployment', 'docker', 'etl', 'etl-pipeline', 'ide', 'jupyter', 'jupyterlab', 'kubernetes', 'machine-learning', 'notebooks', 'orchest', 'pipelines', 'self-hosted'] | ['airflow', 'cloud', 'dag', 'data-pipelines', 'data-science', 'deployment', 'docker', 'etl', 'etl-pipeline', 'ide', 'jupyter', 'jupyterlab', 'kubernetes', 'machine-learning', 'notebooks', 'orchest', 'pipelines', 'self-hosted'] | 2023-06-06 | [('ploomber/ploomber', 0.862511932849884, 'ml-ops', 5), ('mage-ai/mage-ai', 0.7309496402740479, 'ml-ops', 5), ('airbytehq/airbyte', 0.7260224223136902, 'data', 2), ('bodywork-ml/bodywork-core', 0.6645437479019165, 'ml-ops', 3), ('dagster-io/dagster', 0.6582160592079163, 'ml-ops', 3), ('netflix/metaflow', 0.6532567739486694, 'ml-ops', 3), ('flyteorg/flyte', 0.6491807699203491, 'ml-ops', 3), ('zenml-io/zenml', 0.6482174396514893, 'ml-ops', 3), ('backtick-se/cowait', 0.6478020548820496, 'util', 3), ('kubeflow/pipelines', 0.6418040990829468, 'ml-ops', 3), ('kestra-io/kestra', 0.6342953443527222, 'ml-ops', 1), ('linealabs/lineapy', 0.6248028874397278, 'jupyter', 0), ('avaiga/taipy', 0.6196687817573547, 'data', 1), ('dagworks-inc/hamilton', 0.6139141917228699, 'ml-ops', 5), ('meltano/meltano', 0.6131107211112976, 'ml-ops', 2), ('kubeflow-kale/kale', 0.6008453965187073, 'ml-ops', 1), ('darribas/gds_env', 0.5977623462677002, 'gis', 1), ('polyaxon/polyaxon', 0.5914933681488037, 'ml-ops', 6), ('pypa/pipenv', 0.5898057818412781, 'util', 0), ('getindata/kedro-kubeflow', 0.5747969746589661, 'ml-ops', 0), ('apache/airflow', 0.57388836145401, 'ml-ops', 6), ('simonw/datasette', 0.5611031651496887, 'data', 1), ('allegroai/clearml', 0.5581900477409363, 'ml-ops', 1), ('tiangolo/full-stack-fastapi-postgresql', 0.5576450228691101, 'template', 1), ('hi-primus/optimus', 0.5557096004486084, 'ml-ops', 2), ('jina-ai/jina', 0.5534289479255676, 'ml', 3), ('merantix-momentum/squirrel-core', 0.5455144643783569, 'ml', 2), ('astronomer/astro-sdk', 0.5454973578453064, 'ml-ops', 3), ('gefyrahq/gefyra', 0.5443967580795288, 'util', 2), ('featureform/embeddinghub', 0.5408744215965271, 'nlp', 2), ('eventual-inc/daft', 0.5366385579109192, 'pandas', 2), ('polyaxon/datatile', 0.5361875891685486, 'pandas', 1), ('pytest-dev/pytest-testinfra', 0.5304024815559387, 'testing', 2), ('bentoml/bentoml', 0.52862948179245, 'ml-ops', 2), ('zenml-io/mlstacks', 0.5279688239097595, 'ml-ops', 0), ('skypilot-org/skypilot', 0.5275050401687622, 'llm', 2), ('willmcgugan/textual', 0.526781439781189, 'term', 0), ('whylabs/whylogs', 0.5236711502075195, 'util', 2), ('tox-dev/tox', 0.523241400718689, 'testing', 0), ('pydoit/doit', 0.522449254989624, 'util', 1), ('kubeflow/fairing', 0.5192087292671204, 'ml-ops', 0), ('great-expectations/great_expectations', 0.5189650058746338, 'ml-ops', 1), ('koaning/scikit-partial', 0.5157948732376099, 'data', 0), ('streamlit/streamlit', 0.5133765935897827, 'viz', 2), ('fmind/mlops-python-package', 0.5126034617424011, 'template', 0), ('pypa/hatch', 0.510188639163971, 'util', 0), ('localstack/localstack', 0.5089136362075806, 'util', 1), ('thoth-station/micropipenv', 0.5059201121330261, 'util', 0), ('martinheinz/python-project-blueprint', 0.5042280554771423, 'template', 2), ('prefecthq/server', 0.5037789344787598, 'util', 0), ('spotify/luigi', 0.5029619336128235, 'ml-ops', 0), ('prefecthq/prefect', 0.5009967684745789, 'ml-ops', 1), ('fastai/fastcore', 0.5004292130470276, 'util', 0)] | 31 | 4 | null | 0.38 | 2 | 0 | 44 | 7 | 5 | 56 | 5 | 2 | 7 | 90 | 3.5 | 48 |
1,279 | viz | https://github.com/pyqtgraph/pyqtgraph | [] | null | [] | [] | null | null | null | pyqtgraph/pyqtgraph | pyqtgraph | 3,554 | 1,054 | 153 | Python | https://www.pyqtgraph.org | Fast data visualization and GUI tools for scientific / engineering applications | pyqtgraph | 2024-01-14 | 2013-09-12 | 541 | 6.560654 | https://avatars.githubusercontent.com/u/5440571?v=4 | Fast data visualization and GUI tools for scientific / engineering applications | ['numpy', 'qt', 'scientific-visualization', 'visualization'] | ['numpy', 'qt', 'scientific-visualization', 'visualization'] | 2023-12-21 | [('enthought/mayavi', 0.734231173992157, 'viz', 2), ('holoviz/holoviz', 0.6720275282859802, 'viz', 0), ('altair-viz/altair', 0.6555560827255249, 'viz', 1), ('marcomusy/vedo', 0.6459553241729736, 'viz', 3), ('mwaskom/seaborn', 0.6456829905509949, 'viz', 0), ('numpy/numpy', 0.6338181495666504, 'math', 1), ('matplotlib/matplotlib', 0.6332533955574036, 'viz', 1), ('holoviz/panel', 0.6308204531669617, 'viz', 0), ('scitools/iris', 0.6289077401161194, 'gis', 0), ('man-group/dtale', 0.6243569850921631, 'viz', 1), ('contextlab/hypertools', 0.6197599172592163, 'ml', 1), ('pyvista/pyvista', 0.6187593936920166, 'viz', 2), ('holoviz/hvplot', 0.5959815979003906, 'pandas', 0), ('holoviz/datashader', 0.5956966876983643, 'gis', 0), ('vaexio/vaex', 0.592536449432373, 'perf', 1), ('residentmario/geoplot', 0.5889087915420532, 'gis', 0), ('bokeh/bokeh', 0.5844684839248657, 'viz', 1), ('graphistry/pygraphistry', 0.5679578185081482, 'data', 1), ('kanaries/pygwalker', 0.5670520663261414, 'pandas', 1), ('jakevdp/pythondatasciencehandbook', 0.5645138621330261, 'study', 1), ('blaze/blaze', 0.5560141205787659, 'pandas', 0), ('lux-org/lux', 0.5438263416290283, 'viz', 1), ('gregorhd/mapcompare', 0.5434110760688782, 'gis', 0), ('plotly/plotly.py', 0.5377339720726013, 'viz', 1), ('mckinsey/vizro', 0.5374338626861572, 'viz', 1), ('pandas-dev/pandas', 0.5296177268028259, 'pandas', 0), ('mito-ds/monorepo', 0.5288783311843872, 'jupyter', 0), ('wesm/pydata-book', 0.5280112624168396, 'study', 0), ('beeware/toga', 0.5267707109451294, 'gui', 0), ('xl0/lovely-numpy', 0.5235827565193176, 'util', 2), ('pytables/pytables', 0.5200861692428589, 'data', 0), ('districtdatalabs/yellowbrick', 0.5196901559829712, 'ml', 1), ('alexmojaki/heartrate', 0.5196880102157593, 'debug', 1), ('gradio-app/gradio', 0.5161272287368774, 'viz', 0), ('tqdm/tqdm', 0.5137172341346741, 'term', 0), ('bloomberg/ipydatagrid', 0.5133781433105469, 'jupyter', 0), ('dfki-ric/pytransform3d', 0.5104587078094482, 'math', 1), ('wxwidgets/phoenix', 0.509281575679779, 'gui', 0), ('polyaxon/datatile', 0.5080159902572632, 'pandas', 0), ('plotly/dash', 0.5079091191291809, 'viz', 0), ('quantopian/qgrid', 0.5077592730522156, 'jupyter', 0), ('ipython/ipyparallel', 0.5072664022445679, 'perf', 0), ('hoffstadt/dearpygui', 0.5063014626502991, 'gui', 0), ('earthlab/earthpy', 0.506048321723938, 'gis', 0), ('faster-cpython/tools', 0.5059671998023987, 'perf', 0), ('westhealth/pyvis', 0.5053408741950989, 'graph', 0), ('vizzuhq/ipyvizzu', 0.5050925612449646, 'jupyter', 0), ('fastai/fastcore', 0.5031821727752686, 'util', 0), ('makepath/xarray-spatial', 0.503014862537384, 'gis', 0), ('wandb/client', 0.5011712312698364, 'ml', 0), ('nomic-ai/deepscatter', 0.5000237822532654, 'viz', 1)] | 267 | 5 | null | 3.69 | 83 | 42 | 126 | 1 | 2 | 3 | 2 | 83 | 140 | 90 | 1.7 | 48 |
1,060 | ml-rl | https://github.com/deepmind/dm_control | [] | null | [] | [] | null | null | null | deepmind/dm_control | dm_control | 3,414 | 638 | 128 | Python | null | Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. | deepmind | 2024-01-13 | 2017-12-29 | 317 | 10.750337 | https://avatars.githubusercontent.com/u/8596759?v=4 | Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. | ['artificial-intelligence', 'deep-learning', 'machine-learning', 'mujoco', 'neural-networks', 'physics-simulation', 'reinforcement-learning'] | ['artificial-intelligence', 'deep-learning', 'machine-learning', 'mujoco', 'neural-networks', 'physics-simulation', 'reinforcement-learning'] | 2024-01-12 | [('tensorlayer/tensorlayer', 0.654015064239502, 'ml-rl', 3), ('google/trax', 0.6313595771789551, 'ml-dl', 3), ('unity-technologies/ml-agents', 0.6150110363960266, 'ml-rl', 4), ('openai/mujoco-py', 0.5940796732902527, 'sim', 0), ('tensorflow/tensor2tensor', 0.5927808880805969, 'ml', 3), ('keras-rl/keras-rl', 0.590694010257721, 'ml-rl', 3), ('googlecloudplatform/vertex-ai-samples', 0.569223940372467, 'ml', 0), ('google/dopamine', 0.562454104423523, 'ml-rl', 0), ('salesforce/warp-drive', 0.5524762272834778, 'ml-rl', 2), ('bentoml/bentoml', 0.5436348915100098, 'ml-ops', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5414921641349792, 'study', 2), ('deepmind/deepmind-research', 0.5342397689819336, 'ml', 0), ('ddbourgin/numpy-ml', 0.5336541533470154, 'ml', 3), ('determined-ai/determined', 0.5311621427536011, 'ml-ops', 2), ('thu-ml/tianshou', 0.5307285189628601, 'ml-rl', 1), ('wandb/client', 0.5283645987510681, 'ml', 3), ('polyaxon/polyaxon', 0.5232641100883484, 'ml-ops', 4), ('apache/incubator-mxnet', 0.5190567374229431, 'ml-dl', 0), ('explosion/thinc', 0.5177432894706726, 'ml-dl', 3), ('microsoft/deepspeed', 0.5161496996879578, 'ml-dl', 2), ('keras-team/keras', 0.5160452723503113, 'ml-dl', 3), ('onnx/onnx', 0.5158249139785767, 'ml', 2), ('google/brax', 0.514367938041687, 'sim', 2), ('microsoft/onnxruntime', 0.5139978528022766, 'ml', 3), ('pytorchlightning/pytorch-lightning', 0.5117486715316772, 'ml-dl', 3), ('arise-initiative/robosuite', 0.5112686157226562, 'ml-rl', 2), ('denys88/rl_games', 0.5099033117294312, 'ml-rl', 2), ('tensorflow/tensorflow', 0.5096217393875122, 'ml-dl', 2), ('pytorch/rl', 0.507379412651062, 'ml-rl', 2), ('ai4finance-foundation/finrl', 0.5040066838264465, 'finance', 1), ('facebookresearch/habitat-lab', 0.5033023357391357, 'sim', 2), ('adap/flower', 0.502716064453125, 'ml-ops', 3), ('jina-ai/jina', 0.5015859603881836, 'ml', 2), ('deepmodeling/deepmd-kit', 0.5011904239654541, 'sim', 1)] | 41 | 3 | null | 1.04 | 28 | 17 | 74 | 0 | 7 | 4 | 7 | 27 | 34 | 90 | 1.3 | 48 |
1,695 | util | https://github.com/asottile/pyupgrade | ['pre-commit', 'code-quality'] | null | [] | [] | null | null | null | asottile/pyupgrade | pyupgrade | 3,044 | 171 | 35 | Python | null | A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language. | asottile | 2024-01-12 | 2017-02-28 | 361 | 8.432133 | null | A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language. | ['linter', 'pre-commit'] | ['code-quality', 'linter', 'pre-commit'] | 2024-01-08 | [('pre-commit/pre-commit', 0.6998698115348816, 'util', 3), ('callowayproject/bump-my-version', 0.5788130760192871, 'util', 1), ('psf/black', 0.5597057342529297, 'util', 1), ('thudm/codegeex', 0.5523767471313477, 'llm', 0)] | 35 | 4 | null | 1.52 | 23 | 19 | 84 | 0 | 0 | 24 | 24 | 23 | 36 | 90 | 1.6 | 48 |
1,773 | jupyter | https://github.com/jupyter-widgets/ipywidgets | [] | null | [] | [] | null | null | null | jupyter-widgets/ipywidgets | ipywidgets | 2,976 | 942 | 76 | TypeScript | https://ipywidgets.readthedocs.io | Interactive Widgets for the Jupyter Notebook | jupyter-widgets | 2024-01-13 | 2015-04-17 | 458 | 6.48972 | https://avatars.githubusercontent.com/u/25869250?v=4 | Interactive Widgets for the Jupyter Notebook | ['jupyter-notebooks', 'jupyterlab-extension'] | ['jupyter-notebooks', 'jupyterlab-extension'] | 2023-12-19 | [('jupyter/notebook', 0.8538753390312195, 'jupyter', 0), ('voila-dashboards/voila', 0.7114137411117554, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.7082852721214294, 'jupyter', 0), ('jupyter/nbformat', 0.7056740522384644, 'jupyter', 0), ('aws/graph-notebook', 0.687077522277832, 'jupyter', 0), ('bloomberg/ipydatagrid', 0.6741766929626465, 'jupyter', 2), ('vizzuhq/ipyvizzu', 0.6638791561126709, 'jupyter', 0), ('mwouts/jupytext', 0.6579967737197876, 'jupyter', 1), ('maartenbreddels/ipyvolume', 0.6541346311569214, 'jupyter', 0), ('jupyter-widgets/ipyleaflet', 0.6435815095901489, 'gis', 1), ('cohere-ai/notebooks', 0.6400914192199707, 'llm', 0), ('xiaohk/stickyland', 0.6346691250801086, 'jupyter', 1), ('mamba-org/gator', 0.6345846056938171, 'jupyter', 1), ('quantopian/qgrid', 0.6274958848953247, 'jupyter', 0), ('giswqs/mapwidget', 0.6259199976921082, 'gis', 0), ('computationalmodelling/nbval', 0.6210037469863892, 'jupyter', 0), ('jupyterlab/jupyterlab', 0.6188204884529114, 'jupyter', 0), ('chaoleili/jupyterlab_tensorboard', 0.6089569330215454, 'jupyter', 1), ('jupyter/nbconvert', 0.6067924499511719, 'jupyter', 0), ('ipython/ipykernel', 0.6043174862861633, 'util', 0), ('ipython/ipyparallel', 0.5989540219306946, 'perf', 0), ('jupyter-lsp/jupyterlab-lsp', 0.5988917946815491, 'jupyter', 1), ('tkrabel/bamboolib', 0.5929217338562012, 'pandas', 0), ('jupyterlite/jupyterlite', 0.5854482650756836, 'jupyter', 1), ('jupyter/nbdime', 0.5793547034263611, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5679361820220947, 'study', 0), ('jupyter/nbviewer', 0.5482261180877686, 'jupyter', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5422195792198181, 'study', 0), ('koaning/drawdata', 0.5359898805618286, 'jupyter', 0), ('pysimplegui/pysimplegui', 0.5297706127166748, 'gui', 0), ('jupyter/nbgrader', 0.5242305397987366, 'jupyter', 0), ('wxwidgets/phoenix', 0.5223656296730042, 'gui', 0), ('nteract/testbook', 0.5169227719306946, 'jupyter', 0), ('bokeh/bokeh', 0.5115343928337097, 'viz', 0), ('plotly/plotly.py', 0.5096937417984009, 'viz', 0), ('nteract/papermill', 0.5082428455352783, 'jupyter', 0), ('ageron/handson-ml2', 0.5040411949157715, 'ml', 0), ('jupyterlab/jupyter-ai', 0.5035024285316467, 'jupyter', 1)] | 212 | 9 | null | 1.75 | 61 | 5 | 106 | 1 | 5 | 143 | 5 | 61 | 62 | 90 | 1 | 48 |
577 | ml-dl | https://github.com/alpa-projects/alpa | [] | null | [] | [] | null | null | null | alpa-projects/alpa | alpa | 2,921 | 343 | 46 | Python | https://alpa.ai | Training and serving large-scale neural networks with auto parallelization. | alpa-projects | 2024-01-14 | 2021-02-22 | 153 | 19.073694 | https://avatars.githubusercontent.com/u/82711759?v=4 | Training and serving large-scale neural networks with auto parallelization. | ['alpa', 'auto-parallelization', 'compiler', 'deep-learning', 'distributed-computing', 'distributed-training', 'high-performance-computing', 'jax', 'llm', 'machine-learning'] | ['alpa', 'auto-parallelization', 'compiler', 'deep-learning', 'distributed-computing', 'distributed-training', 'high-performance-computing', 'jax', 'llm', 'machine-learning'] | 2023-12-09 | [('paddlepaddle/paddle', 0.7082504034042358, 'ml-dl', 3), ('horovod/horovod', 0.6610665321350098, 'ml-ops', 2), ('microsoft/nni', 0.6484193205833435, 'ml', 2), ('keras-team/autokeras', 0.6452708840370178, 'ml-dl', 2), ('ray-project/ray', 0.6443263292312622, 'ml-ops', 2), ('microsoft/onnxruntime', 0.6340340971946716, 'ml', 2), ('onnx/onnx', 0.6325767636299133, 'ml', 2), ('bigscience-workshop/petals', 0.6318992972373962, 'data', 2), ('hpcaitech/colossalai', 0.6304028034210205, 'llm', 2), ('tensorflow/tensorflow', 0.6228885650634766, 'ml-dl', 2), ('deepmind/dm-haiku', 0.6117678284645081, 'ml-dl', 3), ('keras-team/keras', 0.6106972098350525, 'ml-dl', 3), ('pytorch/glow', 0.6077716946601868, 'ml', 0), ('neuralmagic/deepsparse', 0.6077420711517334, 'nlp', 0), ('microsoft/deepspeed', 0.6055509448051453, 'ml-dl', 2), ('explosion/thinc', 0.5971328020095825, 'ml-dl', 3), ('apache/incubator-mxnet', 0.5928085446357727, 'ml-dl', 0), ('uber/fiber', 0.5923050045967102, 'data', 2), ('google/trax', 0.5918993949890137, 'ml-dl', 3), ('aiqc/aiqc', 0.5878965854644775, 'ml-ops', 0), ('mosaicml/composer', 0.5876685976982117, 'ml-dl', 2), ('bentoml/bentoml', 0.5799961686134338, 'ml-ops', 2), ('awslabs/autogluon', 0.5790343284606934, 'ml', 2), ('huggingface/transformers', 0.57871413230896, 'nlp', 3), ('winedarksea/autots', 0.5752678513526917, 'time-series', 2), ('determined-ai/determined', 0.571927547454834, 'ml-ops', 3), ('nvidia/deeplearningexamples', 0.5713871717453003, 'ml-dl', 1), ('pytorchlightning/pytorch-lightning', 0.5670627951622009, 'ml-dl', 2), ('nccr-itmo/fedot', 0.5670198202133179, 'ml-ops', 1), ('ludwig-ai/ludwig', 0.5621179938316345, 'ml-ops', 3), ('karpathy/micrograd', 0.5604699850082397, 'study', 0), ('neuralmagic/sparseml', 0.560257613658905, 'ml-dl', 0), ('microsoft/flaml', 0.5595440864562988, 'ml', 2), ('ml-tooling/opyrator', 0.5594131350517273, 'viz', 1), ('googlecloudplatform/vertex-ai-samples', 0.5572735667228699, 'ml', 0), ('opentensor/bittensor', 0.556601345539093, 'ml', 2), ('tensorflow/tensor2tensor', 0.5523074865341187, 'ml', 2), ('mlc-ai/mlc-llm', 0.5512301921844482, 'llm', 1), ('ray-project/ray-educational-materials', 0.5506226420402527, 'study', 2), ('polyaxon/polyaxon', 0.5499585866928101, 'ml-ops', 2), ('jina-ai/jina', 0.5415318012237549, 'ml', 2), ('huggingface/autotrain-advanced', 0.5402303338050842, 'ml', 2), ('huggingface/datasets', 0.5375392436981201, 'nlp', 2), ('automl/auto-sklearn', 0.535767674446106, 'ml', 0), ('deepfakes/faceswap', 0.5312798619270325, 'ml-dl', 2), ('uber/petastorm', 0.5310642719268799, 'data', 2), ('adap/flower', 0.5309455990791321, 'ml-ops', 2), ('skypilot-org/skypilot', 0.5307222008705139, 'llm', 3), ('titanml/takeoff', 0.5302854776382446, 'llm', 1), ('mlflow/mlflow', 0.52925705909729, 'ml-ops', 1), ('tlkh/tf-metal-experiments', 0.5292550921440125, 'perf', 1), ('optuna/optuna', 0.5211974382400513, 'ml', 1), ('amanchadha/coursera-deep-learning-specialization', 0.5197420120239258, 'study', 1), ('keras-rl/keras-rl', 0.5167292356491089, 'ml-rl', 1), ('ddbourgin/numpy-ml', 0.5153135657310486, 'ml', 1), ('rafiqhasan/auto-tensorflow', 0.5152245759963989, 'ml-dl', 1), ('google/mediapipe', 0.5148690938949585, 'ml', 2), ('iperov/deepfacelab', 0.511199414730072, 'ml-dl', 2), ('samuela/git-re-basin', 0.507290780544281, 'ml-dl', 3), ('rwightman/pytorch-image-models', 0.507267415523529, 'ml-dl', 1), ('microsoft/semi-supervised-learning', 0.5067688822746277, 'ml', 2), ('unionai-oss/unionml', 0.50523841381073, 'ml-ops', 1), ('nyandwi/modernconvnets', 0.5051084160804749, 'ml-dl', 0), ('tensorlayer/tensorlayer', 0.5041244029998779, 'ml-rl', 1), ('xplainable/xplainable', 0.5035876035690308, 'ml-interpretability', 1), ('bobazooba/xllm', 0.5013929605484009, 'llm', 2), ('towhee-io/towhee', 0.5001883506774902, 'ml-ops', 2)] | 45 | 6 | null | 0.67 | 28 | 6 | 35 | 1 | 1 | 5 | 1 | 28 | 26 | 90 | 0.9 | 48 |
702 | ml | https://github.com/mljar/mljar-supervised | [] | null | [] | [] | null | null | null | mljar/mljar-supervised | mljar-supervised | 2,858 | 372 | 45 | Python | https://mljar.com | Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation | mljar | 2024-01-13 | 2018-11-05 | 273 | 10.463389 | https://avatars.githubusercontent.com/u/20522384?v=4 | Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation | ['automated-machine-learning', 'automatic-machine-learning', 'automl', 'catboost', 'data-science', 'decision-tree', 'ensemble', 'feature-engineering', 'hyper-parameters', 'hyperparameter-optimization', 'lightgbm', 'machine-learning', 'mljar', 'models-tuning', 'neural-network', 'random-forest', 'scikit-learn', 'shap', 'tuning-algorithm', 'xgboost'] | ['automated-machine-learning', 'automatic-machine-learning', 'automl', 'catboost', 'data-science', 'decision-tree', 'ensemble', 'feature-engineering', 'hyper-parameters', 'hyperparameter-optimization', 'lightgbm', 'machine-learning', 'mljar', 'models-tuning', 'neural-network', 'random-forest', 'scikit-learn', 'shap', 'tuning-algorithm', 'xgboost'] | 2024-01-08 | [('microsoft/flaml', 0.7940219044685364, 'ml', 7), ('awslabs/autogluon', 0.7640585899353027, 'ml', 6), ('microsoft/nni', 0.6952623724937439, 'ml', 7), ('automl/auto-sklearn', 0.6622180938720703, 'ml', 4), ('keras-team/autokeras', 0.6605311036109924, 'ml-dl', 3), ('featurelabs/featuretools', 0.645880401134491, 'ml', 6), ('epistasislab/tpot', 0.6145864129066467, 'ml', 8), ('winedarksea/autots', 0.578948438167572, 'time-series', 3), ('nccr-itmo/fedot', 0.5752649307250977, 'ml-ops', 4), ('rasbt/mlxtend', 0.5722818374633789, 'ml', 2), ('google/pyglove', 0.5717796683311462, 'util', 2), ('ray-project/tune-sklearn', 0.5580164194107056, 'ml', 2), ('shankarpandala/lazypredict', 0.5433629155158997, 'ml', 2), ('vaexio/vaex', 0.5394431352615356, 'perf', 2), ('gradio-app/gradio', 0.5382276773452759, 'viz', 2), ('jazzband/tablib', 0.531550407409668, 'data', 0), ('firmai/atspy', 0.5243828296661377, 'time-series', 0), ('paperswithcode/axcell', 0.5180116891860962, 'util', 0), ('dylanhogg/awesome-python', 0.5141728520393372, 'study', 2), ('alkaline-ml/pmdarima', 0.509310781955719, 'time-series', 1), ('google/vizier', 0.5090547204017639, 'ml', 2), ('selfexplainml/piml-toolbox', 0.5052536129951477, 'ml-interpretability', 0), ('ydataai/ydata-synthetic', 0.5016704797744751, 'data', 1), ('astanin/python-tabulate', 0.5009972453117371, 'util', 0)] | 25 | 3 | null | 2.15 | 37 | 11 | 63 | 0 | 6 | 11 | 6 | 37 | 68 | 90 | 1.8 | 48 |
Subsets and Splits